happiness-score-prediction-models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# World Happiness Report Analysis and Prediction\n",
    "\n",
    "Analysed the world happiness dataset and created a prediction model using Linear Regression using the World Happiness Report of the year 2021. <br> \n",
    "Dataset Source: The World Happiness Report 2021"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The World Happiness Report is a publication from the United Nations that contains articles and rankings of national happiness, based on respondent ratings of their own lives, which the report also correlates with various life factors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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   ],
   "source": [
    "defaultW <- getOption(\"warn\") \n",
    "\n",
    "options(warn = -1) \n",
    "#importing libraries\n",
    "library(plyr)\n",
    "library(dplyr)\n",
    "library(tidyverse)\n",
    "library(caTools)\n",
    "library(ggplot2)\n",
    "library(ggthemes)\n",
    "library(reshape2)\n",
    "library(data.table)\n",
    "library(tidyr)\n",
    "library(corrgram)       \n",
    "library(corrplot)\n",
    "options(warn = defaultW)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t149 obs. of  20 variables:\n",
      " $ ï..Country.name                           : chr  \"Finland\" \"Denmark\" \"Switzerland\" \"Iceland\" ...\n",
      " $ Regional.indicator                        : chr  \"Western Europe\" \"Western Europe\" \"Western Europe\" \"Western Europe\" ...\n",
      " $ Ladder.score                              : num  7.84 7.62 7.57 7.55 7.46 ...\n",
      " $ Standard.error.of.ladder.score            : num  0.032 0.035 0.036 0.059 0.027 0.035 0.036 0.037 0.04 0.036 ...\n",
      " $ upperwhisker                              : num  7.9 7.69 7.64 7.67 7.52 ...\n",
      " $ lowerwhisker                              : num  7.78 7.55 7.5 7.44 7.41 ...\n",
      " $ Logged.GDP.per.capita                     : num  10.8 10.9 11.1 10.9 10.9 ...\n",
      " $ Social.support                            : num  0.954 0.954 0.942 0.983 0.942 0.954 0.934 0.908 0.948 0.934 ...\n",
      " $ Healthy.life.expectancy                   : num  72 72.7 74.4 73 72.4 73.3 72.7 72.6 73.4 73.3 ...\n",
      " $ Freedom.to.make.life.choices              : num  0.949 0.946 0.919 0.955 0.913 0.96 0.945 0.907 0.929 0.908 ...\n",
      " $ Generosity                                : num  -0.098 0.03 0.025 0.16 0.175 0.093 0.086 -0.034 0.134 0.042 ...\n",
      " $ Perceptions.of.corruption                 : num  0.186 0.179 0.292 0.673 0.338 0.27 0.237 0.386 0.242 0.481 ...\n",
      " $ Ladder.score.in.Dystopia                  : num  2.43 2.43 2.43 2.43 2.43 2.43 2.43 2.43 2.43 2.43 ...\n",
      " $ Explained.by..Log.GDP.per.capita          : num  1.45 1.5 1.57 1.48 1.5 ...\n",
      " $ Explained.by..Social.support              : num  1.11 1.11 1.08 1.17 1.08 ...\n",
      " $ Explained.by..Healthy.life.expectancy     : num  0.741 0.763 0.816 0.772 0.753 0.782 0.763 0.76 0.785 0.782 ...\n",
      " $ Explained.by..Freedom.to.make.life.choices: num  0.691 0.686 0.653 0.698 0.647 0.703 0.685 0.639 0.665 0.64 ...\n",
      " $ Explained.by..Generosity                  : num  0.124 0.208 0.204 0.293 0.302 0.249 0.244 0.166 0.276 0.215 ...\n",
      " $ Explained.by..Perceptions.of.corruption   : num  0.481 0.485 0.413 0.17 0.384 0.427 0.448 0.353 0.445 0.292 ...\n",
      " $ Dystopia...residual                       : num  3.25 2.87 2.84 2.97 2.8 ...\n"
     ]
    }
   ],
   "source": [
    "#Loading dataset\n",
    "happ <- read.csv(\"world-happiness-report-2021.csv\")\n",
    "str(happ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>149</li><li>20</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 149\n",
       "\\item 20\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 149\n",
       "2. 20\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 149  20"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(happ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "happ <- happ[, -c(2,4,5,6,13,14,15,16,17,18,19)] #dropping unnecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>ï..Country.name</th><th scope=col>Ladder.score</th><th scope=col>Logged.GDP.per.capita</th><th scope=col>Social.support</th><th scope=col>Healthy.life.expectancy</th><th scope=col>Freedom.to.make.life.choices</th><th scope=col>Generosity</th><th scope=col>Perceptions.of.corruption</th><th scope=col>Dystopia...residual</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Finland    </td><td>7.842</td><td>10.775</td><td>0.954</td><td>72.0</td><td>0.949</td><td>-0.098</td><td>0.186</td><td>3.253</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Denmark    </td><td>7.620</td><td>10.933</td><td>0.954</td><td>72.7</td><td>0.946</td><td> 0.030</td><td>0.179</td><td>2.868</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Switzerland</td><td>7.571</td><td>11.117</td><td>0.942</td><td>74.4</td><td>0.919</td><td> 0.025</td><td>0.292</td><td>2.839</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Iceland    </td><td>7.554</td><td>10.878</td><td>0.983</td><td>73.0</td><td>0.955</td><td> 0.160</td><td>0.673</td><td>2.967</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Netherlands</td><td>7.464</td><td>10.932</td><td>0.942</td><td>72.4</td><td>0.913</td><td> 0.175</td><td>0.338</td><td>2.798</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Norway     </td><td>7.392</td><td>11.053</td><td>0.954</td><td>73.3</td><td>0.960</td><td> 0.093</td><td>0.270</td><td>2.580</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & ï..Country.name & Ladder.score & Logged.GDP.per.capita & Social.support & Healthy.life.expectancy & Freedom.to.make.life.choices & Generosity & Perceptions.of.corruption & Dystopia...residual\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & Finland     & 7.842 & 10.775 & 0.954 & 72.0 & 0.949 & -0.098 & 0.186 & 3.253\\\\\n",
       "\t2 & Denmark     & 7.620 & 10.933 & 0.954 & 72.7 & 0.946 &  0.030 & 0.179 & 2.868\\\\\n",
       "\t3 & Switzerland & 7.571 & 11.117 & 0.942 & 74.4 & 0.919 &  0.025 & 0.292 & 2.839\\\\\n",
       "\t4 & Iceland     & 7.554 & 10.878 & 0.983 & 73.0 & 0.955 &  0.160 & 0.673 & 2.967\\\\\n",
       "\t5 & Netherlands & 7.464 & 10.932 & 0.942 & 72.4 & 0.913 &  0.175 & 0.338 & 2.798\\\\\n",
       "\t6 & Norway      & 7.392 & 11.053 & 0.954 & 73.3 & 0.960 &  0.093 & 0.270 & 2.580\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 9\n",
       "\n",
       "| <!--/--> | ï..Country.name &lt;chr&gt; | Ladder.score &lt;dbl&gt; | Logged.GDP.per.capita &lt;dbl&gt; | Social.support &lt;dbl&gt; | Healthy.life.expectancy &lt;dbl&gt; | Freedom.to.make.life.choices &lt;dbl&gt; | Generosity &lt;dbl&gt; | Perceptions.of.corruption &lt;dbl&gt; | Dystopia...residual &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | Finland     | 7.842 | 10.775 | 0.954 | 72.0 | 0.949 | -0.098 | 0.186 | 3.253 |\n",
       "| 2 | Denmark     | 7.620 | 10.933 | 0.954 | 72.7 | 0.946 |  0.030 | 0.179 | 2.868 |\n",
       "| 3 | Switzerland | 7.571 | 11.117 | 0.942 | 74.4 | 0.919 |  0.025 | 0.292 | 2.839 |\n",
       "| 4 | Iceland     | 7.554 | 10.878 | 0.983 | 73.0 | 0.955 |  0.160 | 0.673 | 2.967 |\n",
       "| 5 | Netherlands | 7.464 | 10.932 | 0.942 | 72.4 | 0.913 |  0.175 | 0.338 | 2.798 |\n",
       "| 6 | Norway      | 7.392 | 11.053 | 0.954 | 73.3 | 0.960 |  0.093 | 0.270 | 2.580 |\n",
       "\n"
      ],
      "text/plain": [
       "  ï..Country.name Ladder.score Logged.GDP.per.capita Social.support\n",
       "1 Finland         7.842        10.775                0.954         \n",
       "2 Denmark         7.620        10.933                0.954         \n",
       "3 Switzerland     7.571        11.117                0.942         \n",
       "4 Iceland         7.554        10.878                0.983         \n",
       "5 Netherlands     7.464        10.932                0.942         \n",
       "6 Norway          7.392        11.053                0.954         \n",
       "  Healthy.life.expectancy Freedom.to.make.life.choices Generosity\n",
       "1 72.0                    0.949                        -0.098    \n",
       "2 72.7                    0.946                         0.030    \n",
       "3 74.4                    0.919                         0.025    \n",
       "4 73.0                    0.955                         0.160    \n",
       "5 72.4                    0.913                         0.175    \n",
       "6 73.3                    0.960                         0.093    \n",
       "  Perceptions.of.corruption Dystopia...residual\n",
       "1 0.186                     3.253              \n",
       "2 0.179                     2.868              \n",
       "3 0.292                     2.839              \n",
       "4 0.673                     2.967              \n",
       "5 0.338                     2.798              \n",
       "6 0.270                     2.580              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(happ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Renaming columns for convenience\n",
    "happ <- happ %>% rename(c(\"Country\" = \"ï..Country.name\" ,\n",
    "                      \"Score\" = \"Ladder.score\",\n",
    "                      \"GDP\" = \"Logged.GDP.per.capita\", \n",
    "                      \"Family\" = \"Social.support\",\n",
    "                      \"Health\" = \"Healthy.life.expectancy\", \n",
    "                      \"Freedom\" = \"Freedom.to.make.life.choices\",\n",
    "                      \"Corruption\" = \"Perceptions.of.corruption\",\n",
    "                         \"Dystopia.residual\" = \"Dystopia...residual\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Country</th><th scope=col>Score</th><th scope=col>GDP</th><th scope=col>Family</th><th scope=col>Health</th><th scope=col>Freedom</th><th scope=col>Generosity</th><th scope=col>Corruption</th><th scope=col>Dystopia.residual</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Finland    </td><td>7.842</td><td>10.775</td><td>0.954</td><td>72.0</td><td>0.949</td><td>-0.098</td><td>0.186</td><td>3.253</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Denmark    </td><td>7.620</td><td>10.933</td><td>0.954</td><td>72.7</td><td>0.946</td><td> 0.030</td><td>0.179</td><td>2.868</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Switzerland</td><td>7.571</td><td>11.117</td><td>0.942</td><td>74.4</td><td>0.919</td><td> 0.025</td><td>0.292</td><td>2.839</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Iceland    </td><td>7.554</td><td>10.878</td><td>0.983</td><td>73.0</td><td>0.955</td><td> 0.160</td><td>0.673</td><td>2.967</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Netherlands</td><td>7.464</td><td>10.932</td><td>0.942</td><td>72.4</td><td>0.913</td><td> 0.175</td><td>0.338</td><td>2.798</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Norway     </td><td>7.392</td><td>11.053</td><td>0.954</td><td>73.3</td><td>0.960</td><td> 0.093</td><td>0.270</td><td>2.580</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & Country & Score & GDP & Family & Health & Freedom & Generosity & Corruption & Dystopia.residual\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & Finland     & 7.842 & 10.775 & 0.954 & 72.0 & 0.949 & -0.098 & 0.186 & 3.253\\\\\n",
       "\t2 & Denmark     & 7.620 & 10.933 & 0.954 & 72.7 & 0.946 &  0.030 & 0.179 & 2.868\\\\\n",
       "\t3 & Switzerland & 7.571 & 11.117 & 0.942 & 74.4 & 0.919 &  0.025 & 0.292 & 2.839\\\\\n",
       "\t4 & Iceland     & 7.554 & 10.878 & 0.983 & 73.0 & 0.955 &  0.160 & 0.673 & 2.967\\\\\n",
       "\t5 & Netherlands & 7.464 & 10.932 & 0.942 & 72.4 & 0.913 &  0.175 & 0.338 & 2.798\\\\\n",
       "\t6 & Norway      & 7.392 & 11.053 & 0.954 & 73.3 & 0.960 &  0.093 & 0.270 & 2.580\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 9\n",
       "\n",
       "| <!--/--> | Country &lt;chr&gt; | Score &lt;dbl&gt; | GDP &lt;dbl&gt; | Family &lt;dbl&gt; | Health &lt;dbl&gt; | Freedom &lt;dbl&gt; | Generosity &lt;dbl&gt; | Corruption &lt;dbl&gt; | Dystopia.residual &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | Finland     | 7.842 | 10.775 | 0.954 | 72.0 | 0.949 | -0.098 | 0.186 | 3.253 |\n",
       "| 2 | Denmark     | 7.620 | 10.933 | 0.954 | 72.7 | 0.946 |  0.030 | 0.179 | 2.868 |\n",
       "| 3 | Switzerland | 7.571 | 11.117 | 0.942 | 74.4 | 0.919 |  0.025 | 0.292 | 2.839 |\n",
       "| 4 | Iceland     | 7.554 | 10.878 | 0.983 | 73.0 | 0.955 |  0.160 | 0.673 | 2.967 |\n",
       "| 5 | Netherlands | 7.464 | 10.932 | 0.942 | 72.4 | 0.913 |  0.175 | 0.338 | 2.798 |\n",
       "| 6 | Norway      | 7.392 | 11.053 | 0.954 | 73.3 | 0.960 |  0.093 | 0.270 | 2.580 |\n",
       "\n"
      ],
      "text/plain": [
       "  Country     Score GDP    Family Health Freedom Generosity Corruption\n",
       "1 Finland     7.842 10.775 0.954  72.0   0.949   -0.098     0.186     \n",
       "2 Denmark     7.620 10.933 0.954  72.7   0.946    0.030     0.179     \n",
       "3 Switzerland 7.571 11.117 0.942  74.4   0.919    0.025     0.292     \n",
       "4 Iceland     7.554 10.878 0.983  73.0   0.955    0.160     0.673     \n",
       "5 Netherlands 7.464 10.932 0.942  72.4   0.913    0.175     0.338     \n",
       "6 Norway      7.392 11.053 0.954  73.3   0.960    0.093     0.270     \n",
       "  Dystopia.residual\n",
       "1 3.253            \n",
       "2 2.868            \n",
       "3 2.839            \n",
       "4 2.967            \n",
       "5 2.798            \n",
       "6 2.580            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(happ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 149 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Country</th><th scope=col>Score</th><th scope=col>GDP</th><th scope=col>Family</th><th scope=col>Health</th><th scope=col>Freedom</th><th scope=col>Generosity</th><th scope=col>Corruption</th><th scope=col>Dystopia.residual</th><th scope=col>Rank</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Finland                 </td><td>7.842</td><td>10.775</td><td>0.954</td><td>72.000</td><td>0.949</td><td>-0.098</td><td>0.186</td><td>3.253</td><td> 1</td></tr>\n",
       "\t<tr><td>Denmark                 </td><td>7.620</td><td>10.933</td><td>0.954</td><td>72.700</td><td>0.946</td><td> 0.030</td><td>0.179</td><td>2.868</td><td> 2</td></tr>\n",
       "\t<tr><td>Switzerland             </td><td>7.571</td><td>11.117</td><td>0.942</td><td>74.400</td><td>0.919</td><td> 0.025</td><td>0.292</td><td>2.839</td><td> 3</td></tr>\n",
       "\t<tr><td>Iceland                 </td><td>7.554</td><td>10.878</td><td>0.983</td><td>73.000</td><td>0.955</td><td> 0.160</td><td>0.673</td><td>2.967</td><td> 4</td></tr>\n",
       "\t<tr><td>Netherlands             </td><td>7.464</td><td>10.932</td><td>0.942</td><td>72.400</td><td>0.913</td><td> 0.175</td><td>0.338</td><td>2.798</td><td> 5</td></tr>\n",
       "\t<tr><td>Norway                  </td><td>7.392</td><td>11.053</td><td>0.954</td><td>73.300</td><td>0.960</td><td> 0.093</td><td>0.270</td><td>2.580</td><td> 6</td></tr>\n",
       "\t<tr><td>Sweden                  </td><td>7.363</td><td>10.867</td><td>0.934</td><td>72.700</td><td>0.945</td><td> 0.086</td><td>0.237</td><td>2.683</td><td> 7</td></tr>\n",
       "\t<tr><td>Luxembourg              </td><td>7.324</td><td>11.647</td><td>0.908</td><td>72.600</td><td>0.907</td><td>-0.034</td><td>0.386</td><td>2.653</td><td> 8</td></tr>\n",
       "\t<tr><td>New Zealand             </td><td>7.277</td><td>10.643</td><td>0.948</td><td>73.400</td><td>0.929</td><td> 0.134</td><td>0.242</td><td>2.612</td><td> 9</td></tr>\n",
       "\t<tr><td>Austria                 </td><td>7.268</td><td>10.906</td><td>0.934</td><td>73.300</td><td>0.908</td><td> 0.042</td><td>0.481</td><td>2.784</td><td>10</td></tr>\n",
       "\t<tr><td>Australia               </td><td>7.183</td><td>10.796</td><td>0.940</td><td>73.900</td><td>0.914</td><td> 0.159</td><td>0.442</td><td>2.598</td><td>11</td></tr>\n",
       "\t<tr><td>Israel                  </td><td>7.157</td><td>10.575</td><td>0.939</td><td>73.503</td><td>0.800</td><td> 0.031</td><td>0.753</td><td>3.083</td><td>12</td></tr>\n",
       "\t<tr><td>Germany                 </td><td>7.155</td><td>10.873</td><td>0.903</td><td>72.500</td><td>0.875</td><td> 0.011</td><td>0.460</td><td>2.824</td><td>13</td></tr>\n",
       "\t<tr><td>Canada                  </td><td>7.103</td><td>10.776</td><td>0.926</td><td>73.800</td><td>0.915</td><td> 0.089</td><td>0.415</td><td>2.585</td><td>14</td></tr>\n",
       "\t<tr><td>Ireland                 </td><td>7.085</td><td>11.342</td><td>0.947</td><td>72.400</td><td>0.879</td><td> 0.077</td><td>0.363</td><td>2.384</td><td>15</td></tr>\n",
       "\t<tr><td>Costa Rica              </td><td>7.069</td><td> 9.880</td><td>0.891</td><td>71.400</td><td>0.934</td><td>-0.126</td><td>0.809</td><td>3.387</td><td>16</td></tr>\n",
       "\t<tr><td>United Kingdom          </td><td>7.064</td><td>10.707</td><td>0.934</td><td>72.500</td><td>0.859</td><td> 0.233</td><td>0.459</td><td>2.596</td><td>17</td></tr>\n",
       "\t<tr><td>Czech Republic          </td><td>6.965</td><td>10.556</td><td>0.947</td><td>70.807</td><td>0.858</td><td>-0.208</td><td>0.868</td><td>3.124</td><td>18</td></tr>\n",
       "\t<tr><td>United States           </td><td>6.951</td><td>11.023</td><td>0.920</td><td>68.200</td><td>0.837</td><td> 0.098</td><td>0.698</td><td>2.807</td><td>19</td></tr>\n",
       "\t<tr><td>Belgium                 </td><td>6.834</td><td>10.823</td><td>0.906</td><td>72.199</td><td>0.783</td><td>-0.153</td><td>0.646</td><td>2.862</td><td>20</td></tr>\n",
       "\t<tr><td>France                  </td><td>6.690</td><td>10.704</td><td>0.942</td><td>74.000</td><td>0.822</td><td>-0.147</td><td>0.571</td><td>2.521</td><td>21</td></tr>\n",
       "\t<tr><td>Bahrain                 </td><td>6.647</td><td>10.669</td><td>0.862</td><td>69.495</td><td>0.925</td><td> 0.089</td><td>0.722</td><td>2.631</td><td>22</td></tr>\n",
       "\t<tr><td>Malta                   </td><td>6.602</td><td>10.674</td><td>0.931</td><td>72.200</td><td>0.927</td><td> 0.133</td><td>0.653</td><td>2.268</td><td>23</td></tr>\n",
       "\t<tr><td>Taiwan Province of China</td><td>6.584</td><td>10.871</td><td>0.898</td><td>69.600</td><td>0.784</td><td>-0.070</td><td>0.721</td><td>2.687</td><td>24</td></tr>\n",
       "\t<tr><td>United Arab Emirates    </td><td>6.561</td><td>11.085</td><td>0.844</td><td>67.333</td><td>0.932</td><td> 0.074</td><td>0.589</td><td>2.422</td><td>25</td></tr>\n",
       "\t<tr><td>Saudi Arabia            </td><td>6.494</td><td>10.743</td><td>0.891</td><td>66.603</td><td>0.877</td><td>-0.149</td><td>0.684</td><td>2.668</td><td>26</td></tr>\n",
       "\t<tr><td>Spain                   </td><td>6.491</td><td>10.571</td><td>0.932</td><td>74.700</td><td>0.761</td><td>-0.081</td><td>0.745</td><td>2.513</td><td>27</td></tr>\n",
       "\t<tr><td>Italy                   </td><td>6.483</td><td>10.623</td><td>0.880</td><td>73.800</td><td>0.693</td><td>-0.084</td><td>0.866</td><td>2.794</td><td>28</td></tr>\n",
       "\t<tr><td>Slovenia                </td><td>6.461</td><td>10.529</td><td>0.948</td><td>71.400</td><td>0.949</td><td>-0.101</td><td>0.806</td><td>2.388</td><td>29</td></tr>\n",
       "\t<tr><td>Guatemala               </td><td>6.435</td><td> 9.053</td><td>0.813</td><td>64.958</td><td>0.906</td><td>-0.038</td><td>0.775</td><td>3.375</td><td>30</td></tr>\n",
       "\t<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "\t<tr><td>Liberia                </td><td>4.625</td><td>7.288</td><td>0.720</td><td>56.498</td><td>0.735</td><td> 0.050</td><td>0.850</td><td>2.857</td><td>120</td></tr>\n",
       "\t<tr><td>Kenya                  </td><td>4.607</td><td>8.361</td><td>0.688</td><td>60.704</td><td>0.779</td><td> 0.287</td><td>0.825</td><td>2.180</td><td>121</td></tr>\n",
       "\t<tr><td>Tunisia                </td><td>4.596</td><td>9.266</td><td>0.691</td><td>67.201</td><td>0.656</td><td>-0.201</td><td>0.870</td><td>2.138</td><td>122</td></tr>\n",
       "\t<tr><td>Lebanon                </td><td>4.584</td><td>9.626</td><td>0.848</td><td>67.355</td><td>0.525</td><td>-0.073</td><td>0.898</td><td>1.736</td><td>123</td></tr>\n",
       "\t<tr><td>Namibia                </td><td>4.574</td><td>9.161</td><td>0.818</td><td>56.799</td><td>0.719</td><td>-0.149</td><td>0.847</td><td>2.068</td><td>124</td></tr>\n",
       "\t<tr><td>Palestinian Territories</td><td>4.517</td><td>8.485</td><td>0.826</td><td>62.250</td><td>0.653</td><td>-0.163</td><td>0.821</td><td>2.131</td><td>125</td></tr>\n",
       "\t<tr><td>Myanmar                </td><td>4.426</td><td>8.541</td><td>0.779</td><td>59.302</td><td>0.876</td><td> 0.509</td><td>0.660</td><td>1.407</td><td>126</td></tr>\n",
       "\t<tr><td>Jordan                 </td><td>4.395</td><td>9.182</td><td>0.767</td><td>67.000</td><td>0.755</td><td>-0.167</td><td>0.705</td><td>1.553</td><td>127</td></tr>\n",
       "\t<tr><td>Chad                   </td><td>4.355</td><td>7.364</td><td>0.619</td><td>48.478</td><td>0.579</td><td> 0.041</td><td>0.807</td><td>3.209</td><td>128</td></tr>\n",
       "\t<tr><td>Sri Lanka              </td><td>4.325</td><td>9.470</td><td>0.827</td><td>67.299</td><td>0.841</td><td> 0.079</td><td>0.863</td><td>1.075</td><td>129</td></tr>\n",
       "\t<tr><td>Swaziland              </td><td>4.308</td><td>9.065</td><td>0.770</td><td>50.833</td><td>0.647</td><td>-0.185</td><td>0.708</td><td>2.155</td><td>130</td></tr>\n",
       "\t<tr><td>Comoros                </td><td>4.289</td><td>8.031</td><td>0.626</td><td>57.349</td><td>0.548</td><td> 0.082</td><td>0.781</td><td>2.610</td><td>131</td></tr>\n",
       "\t<tr><td>Egypt                  </td><td>4.283</td><td>9.367</td><td>0.750</td><td>61.998</td><td>0.749</td><td>-0.182</td><td>0.795</td><td>1.648</td><td>132</td></tr>\n",
       "\t<tr><td>Ethiopia               </td><td>4.275</td><td>7.694</td><td>0.764</td><td>59.000</td><td>0.752</td><td> 0.082</td><td>0.761</td><td>2.089</td><td>133</td></tr>\n",
       "\t<tr><td>Mauritania             </td><td>4.227</td><td>8.542</td><td>0.795</td><td>57.161</td><td>0.561</td><td>-0.106</td><td>0.731</td><td>2.069</td><td>134</td></tr>\n",
       "\t<tr><td>Madagascar             </td><td>4.208</td><td>7.396</td><td>0.686</td><td>59.305</td><td>0.552</td><td>-0.005</td><td>0.803</td><td>2.620</td><td>135</td></tr>\n",
       "\t<tr><td>Togo                   </td><td>4.107</td><td>7.362</td><td>0.569</td><td>54.914</td><td>0.619</td><td> 0.032</td><td>0.772</td><td>2.806</td><td>136</td></tr>\n",
       "\t<tr><td>Zambia                 </td><td>4.073</td><td>8.145</td><td>0.708</td><td>55.809</td><td>0.782</td><td> 0.061</td><td>0.823</td><td>1.975</td><td>137</td></tr>\n",
       "\t<tr><td>Sierra Leone           </td><td>3.849</td><td>7.434</td><td>0.630</td><td>51.651</td><td>0.717</td><td> 0.084</td><td>0.866</td><td>2.396</td><td>138</td></tr>\n",
       "\t<tr><td>India                  </td><td>3.819</td><td>8.755</td><td>0.603</td><td>60.633</td><td>0.893</td><td> 0.089</td><td>0.774</td><td>1.405</td><td>139</td></tr>\n",
       "\t<tr><td>Burundi                </td><td>3.775</td><td>6.635</td><td>0.490</td><td>53.400</td><td>0.626</td><td>-0.024</td><td>0.607</td><td>2.876</td><td>140</td></tr>\n",
       "\t<tr><td>Yemen                  </td><td>3.658</td><td>7.578</td><td>0.832</td><td>57.122</td><td>0.602</td><td>-0.147</td><td>0.800</td><td>1.776</td><td>141</td></tr>\n",
       "\t<tr><td>Tanzania               </td><td>3.623</td><td>7.876</td><td>0.702</td><td>57.999</td><td>0.833</td><td> 0.183</td><td>0.577</td><td>1.263</td><td>142</td></tr>\n",
       "\t<tr><td>Haiti                  </td><td>3.615</td><td>7.477</td><td>0.540</td><td>55.700</td><td>0.593</td><td> 0.422</td><td>0.721</td><td>2.060</td><td>143</td></tr>\n",
       "\t<tr><td>Malawi                 </td><td>3.600</td><td>6.958</td><td>0.537</td><td>57.948</td><td>0.780</td><td> 0.038</td><td>0.729</td><td>2.190</td><td>144</td></tr>\n",
       "\t<tr><td>Lesotho                </td><td>3.512</td><td>7.926</td><td>0.787</td><td>48.700</td><td>0.715</td><td>-0.131</td><td>0.915</td><td>1.800</td><td>145</td></tr>\n",
       "\t<tr><td>Botswana               </td><td>3.467</td><td>9.782</td><td>0.784</td><td>59.269</td><td>0.824</td><td>-0.246</td><td>0.801</td><td>0.648</td><td>146</td></tr>\n",
       "\t<tr><td>Rwanda                 </td><td>3.415</td><td>7.676</td><td>0.552</td><td>61.400</td><td>0.897</td><td> 0.061</td><td>0.167</td><td>1.095</td><td>147</td></tr>\n",
       "\t<tr><td>Zimbabwe               </td><td>3.145</td><td>7.943</td><td>0.750</td><td>56.201</td><td>0.677</td><td>-0.047</td><td>0.821</td><td>1.205</td><td>148</td></tr>\n",
       "\t<tr><td>Afghanistan            </td><td>2.523</td><td>7.695</td><td>0.463</td><td>52.493</td><td>0.382</td><td>-0.102</td><td>0.924</td><td>1.895</td><td>149</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 149 × 10\n",
       "\\begin{tabular}{llllllllll}\n",
       " Country & Score & GDP & Family & Health & Freedom & Generosity & Corruption & Dystopia.residual & Rank\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int>\\\\\n",
       "\\hline\n",
       "\t Finland                  & 7.842 & 10.775 & 0.954 & 72.000 & 0.949 & -0.098 & 0.186 & 3.253 &  1\\\\\n",
       "\t Denmark                  & 7.620 & 10.933 & 0.954 & 72.700 & 0.946 &  0.030 & 0.179 & 2.868 &  2\\\\\n",
       "\t Switzerland              & 7.571 & 11.117 & 0.942 & 74.400 & 0.919 &  0.025 & 0.292 & 2.839 &  3\\\\\n",
       "\t Iceland                  & 7.554 & 10.878 & 0.983 & 73.000 & 0.955 &  0.160 & 0.673 & 2.967 &  4\\\\\n",
       "\t Netherlands              & 7.464 & 10.932 & 0.942 & 72.400 & 0.913 &  0.175 & 0.338 & 2.798 &  5\\\\\n",
       "\t Norway                   & 7.392 & 11.053 & 0.954 & 73.300 & 0.960 &  0.093 & 0.270 & 2.580 &  6\\\\\n",
       "\t Sweden                   & 7.363 & 10.867 & 0.934 & 72.700 & 0.945 &  0.086 & 0.237 & 2.683 &  7\\\\\n",
       "\t Luxembourg               & 7.324 & 11.647 & 0.908 & 72.600 & 0.907 & -0.034 & 0.386 & 2.653 &  8\\\\\n",
       "\t New Zealand              & 7.277 & 10.643 & 0.948 & 73.400 & 0.929 &  0.134 & 0.242 & 2.612 &  9\\\\\n",
       "\t Austria                  & 7.268 & 10.906 & 0.934 & 73.300 & 0.908 &  0.042 & 0.481 & 2.784 & 10\\\\\n",
       "\t Australia                & 7.183 & 10.796 & 0.940 & 73.900 & 0.914 &  0.159 & 0.442 & 2.598 & 11\\\\\n",
       "\t Israel                   & 7.157 & 10.575 & 0.939 & 73.503 & 0.800 &  0.031 & 0.753 & 3.083 & 12\\\\\n",
       "\t Germany                  & 7.155 & 10.873 & 0.903 & 72.500 & 0.875 &  0.011 & 0.460 & 2.824 & 13\\\\\n",
       "\t Canada                   & 7.103 & 10.776 & 0.926 & 73.800 & 0.915 &  0.089 & 0.415 & 2.585 & 14\\\\\n",
       "\t Ireland                  & 7.085 & 11.342 & 0.947 & 72.400 & 0.879 &  0.077 & 0.363 & 2.384 & 15\\\\\n",
       "\t Costa Rica               & 7.069 &  9.880 & 0.891 & 71.400 & 0.934 & -0.126 & 0.809 & 3.387 & 16\\\\\n",
       "\t United Kingdom           & 7.064 & 10.707 & 0.934 & 72.500 & 0.859 &  0.233 & 0.459 & 2.596 & 17\\\\\n",
       "\t Czech Republic           & 6.965 & 10.556 & 0.947 & 70.807 & 0.858 & -0.208 & 0.868 & 3.124 & 18\\\\\n",
       "\t United States            & 6.951 & 11.023 & 0.920 & 68.200 & 0.837 &  0.098 & 0.698 & 2.807 & 19\\\\\n",
       "\t Belgium                  & 6.834 & 10.823 & 0.906 & 72.199 & 0.783 & -0.153 & 0.646 & 2.862 & 20\\\\\n",
       "\t France                   & 6.690 & 10.704 & 0.942 & 74.000 & 0.822 & -0.147 & 0.571 & 2.521 & 21\\\\\n",
       "\t Bahrain                  & 6.647 & 10.669 & 0.862 & 69.495 & 0.925 &  0.089 & 0.722 & 2.631 & 22\\\\\n",
       "\t Malta                    & 6.602 & 10.674 & 0.931 & 72.200 & 0.927 &  0.133 & 0.653 & 2.268 & 23\\\\\n",
       "\t Taiwan Province of China & 6.584 & 10.871 & 0.898 & 69.600 & 0.784 & -0.070 & 0.721 & 2.687 & 24\\\\\n",
       "\t United Arab Emirates     & 6.561 & 11.085 & 0.844 & 67.333 & 0.932 &  0.074 & 0.589 & 2.422 & 25\\\\\n",
       "\t Saudi Arabia             & 6.494 & 10.743 & 0.891 & 66.603 & 0.877 & -0.149 & 0.684 & 2.668 & 26\\\\\n",
       "\t Spain                    & 6.491 & 10.571 & 0.932 & 74.700 & 0.761 & -0.081 & 0.745 & 2.513 & 27\\\\\n",
       "\t Italy                    & 6.483 & 10.623 & 0.880 & 73.800 & 0.693 & -0.084 & 0.866 & 2.794 & 28\\\\\n",
       "\t Slovenia                 & 6.461 & 10.529 & 0.948 & 71.400 & 0.949 & -0.101 & 0.806 & 2.388 & 29\\\\\n",
       "\t Guatemala                & 6.435 &  9.053 & 0.813 & 64.958 & 0.906 & -0.038 & 0.775 & 3.375 & 30\\\\\n",
       "\t ... & ... & ... & ... & ... & ... & ... & ... & ... & ...\\\\\n",
       "\t Liberia                 & 4.625 & 7.288 & 0.720 & 56.498 & 0.735 &  0.050 & 0.850 & 2.857 & 120\\\\\n",
       "\t Kenya                   & 4.607 & 8.361 & 0.688 & 60.704 & 0.779 &  0.287 & 0.825 & 2.180 & 121\\\\\n",
       "\t Tunisia                 & 4.596 & 9.266 & 0.691 & 67.201 & 0.656 & -0.201 & 0.870 & 2.138 & 122\\\\\n",
       "\t Lebanon                 & 4.584 & 9.626 & 0.848 & 67.355 & 0.525 & -0.073 & 0.898 & 1.736 & 123\\\\\n",
       "\t Namibia                 & 4.574 & 9.161 & 0.818 & 56.799 & 0.719 & -0.149 & 0.847 & 2.068 & 124\\\\\n",
       "\t Palestinian Territories & 4.517 & 8.485 & 0.826 & 62.250 & 0.653 & -0.163 & 0.821 & 2.131 & 125\\\\\n",
       "\t Myanmar                 & 4.426 & 8.541 & 0.779 & 59.302 & 0.876 &  0.509 & 0.660 & 1.407 & 126\\\\\n",
       "\t Jordan                  & 4.395 & 9.182 & 0.767 & 67.000 & 0.755 & -0.167 & 0.705 & 1.553 & 127\\\\\n",
       "\t Chad                    & 4.355 & 7.364 & 0.619 & 48.478 & 0.579 &  0.041 & 0.807 & 3.209 & 128\\\\\n",
       "\t Sri Lanka               & 4.325 & 9.470 & 0.827 & 67.299 & 0.841 &  0.079 & 0.863 & 1.075 & 129\\\\\n",
       "\t Swaziland               & 4.308 & 9.065 & 0.770 & 50.833 & 0.647 & -0.185 & 0.708 & 2.155 & 130\\\\\n",
       "\t Comoros                 & 4.289 & 8.031 & 0.626 & 57.349 & 0.548 &  0.082 & 0.781 & 2.610 & 131\\\\\n",
       "\t Egypt                   & 4.283 & 9.367 & 0.750 & 61.998 & 0.749 & -0.182 & 0.795 & 1.648 & 132\\\\\n",
       "\t Ethiopia                & 4.275 & 7.694 & 0.764 & 59.000 & 0.752 &  0.082 & 0.761 & 2.089 & 133\\\\\n",
       "\t Mauritania              & 4.227 & 8.542 & 0.795 & 57.161 & 0.561 & -0.106 & 0.731 & 2.069 & 134\\\\\n",
       "\t Madagascar              & 4.208 & 7.396 & 0.686 & 59.305 & 0.552 & -0.005 & 0.803 & 2.620 & 135\\\\\n",
       "\t Togo                    & 4.107 & 7.362 & 0.569 & 54.914 & 0.619 &  0.032 & 0.772 & 2.806 & 136\\\\\n",
       "\t Zambia                  & 4.073 & 8.145 & 0.708 & 55.809 & 0.782 &  0.061 & 0.823 & 1.975 & 137\\\\\n",
       "\t Sierra Leone            & 3.849 & 7.434 & 0.630 & 51.651 & 0.717 &  0.084 & 0.866 & 2.396 & 138\\\\\n",
       "\t India                   & 3.819 & 8.755 & 0.603 & 60.633 & 0.893 &  0.089 & 0.774 & 1.405 & 139\\\\\n",
       "\t Burundi                 & 3.775 & 6.635 & 0.490 & 53.400 & 0.626 & -0.024 & 0.607 & 2.876 & 140\\\\\n",
       "\t Yemen                   & 3.658 & 7.578 & 0.832 & 57.122 & 0.602 & -0.147 & 0.800 & 1.776 & 141\\\\\n",
       "\t Tanzania                & 3.623 & 7.876 & 0.702 & 57.999 & 0.833 &  0.183 & 0.577 & 1.263 & 142\\\\\n",
       "\t Haiti                   & 3.615 & 7.477 & 0.540 & 55.700 & 0.593 &  0.422 & 0.721 & 2.060 & 143\\\\\n",
       "\t Malawi                  & 3.600 & 6.958 & 0.537 & 57.948 & 0.780 &  0.038 & 0.729 & 2.190 & 144\\\\\n",
       "\t Lesotho                 & 3.512 & 7.926 & 0.787 & 48.700 & 0.715 & -0.131 & 0.915 & 1.800 & 145\\\\\n",
       "\t Botswana                & 3.467 & 9.782 & 0.784 & 59.269 & 0.824 & -0.246 & 0.801 & 0.648 & 146\\\\\n",
       "\t Rwanda                  & 3.415 & 7.676 & 0.552 & 61.400 & 0.897 &  0.061 & 0.167 & 1.095 & 147\\\\\n",
       "\t Zimbabwe                & 3.145 & 7.943 & 0.750 & 56.201 & 0.677 & -0.047 & 0.821 & 1.205 & 148\\\\\n",
       "\t Afghanistan             & 2.523 & 7.695 & 0.463 & 52.493 & 0.382 & -0.102 & 0.924 & 1.895 & 149\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 149 × 10\n",
       "\n",
       "| Country &lt;chr&gt; | Score &lt;dbl&gt; | GDP &lt;dbl&gt; | Family &lt;dbl&gt; | Health &lt;dbl&gt; | Freedom &lt;dbl&gt; | Generosity &lt;dbl&gt; | Corruption &lt;dbl&gt; | Dystopia.residual &lt;dbl&gt; | Rank &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| Finland                  | 7.842 | 10.775 | 0.954 | 72.000 | 0.949 | -0.098 | 0.186 | 3.253 |  1 |\n",
       "| Denmark                  | 7.620 | 10.933 | 0.954 | 72.700 | 0.946 |  0.030 | 0.179 | 2.868 |  2 |\n",
       "| Switzerland              | 7.571 | 11.117 | 0.942 | 74.400 | 0.919 |  0.025 | 0.292 | 2.839 |  3 |\n",
       "| Iceland                  | 7.554 | 10.878 | 0.983 | 73.000 | 0.955 |  0.160 | 0.673 | 2.967 |  4 |\n",
       "| Netherlands              | 7.464 | 10.932 | 0.942 | 72.400 | 0.913 |  0.175 | 0.338 | 2.798 |  5 |\n",
       "| Norway                   | 7.392 | 11.053 | 0.954 | 73.300 | 0.960 |  0.093 | 0.270 | 2.580 |  6 |\n",
       "| Sweden                   | 7.363 | 10.867 | 0.934 | 72.700 | 0.945 |  0.086 | 0.237 | 2.683 |  7 |\n",
       "| Luxembourg               | 7.324 | 11.647 | 0.908 | 72.600 | 0.907 | -0.034 | 0.386 | 2.653 |  8 |\n",
       "| New Zealand              | 7.277 | 10.643 | 0.948 | 73.400 | 0.929 |  0.134 | 0.242 | 2.612 |  9 |\n",
       "| Austria                  | 7.268 | 10.906 | 0.934 | 73.300 | 0.908 |  0.042 | 0.481 | 2.784 | 10 |\n",
       "| Australia                | 7.183 | 10.796 | 0.940 | 73.900 | 0.914 |  0.159 | 0.442 | 2.598 | 11 |\n",
       "| Israel                   | 7.157 | 10.575 | 0.939 | 73.503 | 0.800 |  0.031 | 0.753 | 3.083 | 12 |\n",
       "| Germany                  | 7.155 | 10.873 | 0.903 | 72.500 | 0.875 |  0.011 | 0.460 | 2.824 | 13 |\n",
       "| Canada                   | 7.103 | 10.776 | 0.926 | 73.800 | 0.915 |  0.089 | 0.415 | 2.585 | 14 |\n",
       "| Ireland                  | 7.085 | 11.342 | 0.947 | 72.400 | 0.879 |  0.077 | 0.363 | 2.384 | 15 |\n",
       "| Costa Rica               | 7.069 |  9.880 | 0.891 | 71.400 | 0.934 | -0.126 | 0.809 | 3.387 | 16 |\n",
       "| United Kingdom           | 7.064 | 10.707 | 0.934 | 72.500 | 0.859 |  0.233 | 0.459 | 2.596 | 17 |\n",
       "| Czech Republic           | 6.965 | 10.556 | 0.947 | 70.807 | 0.858 | -0.208 | 0.868 | 3.124 | 18 |\n",
       "| United States            | 6.951 | 11.023 | 0.920 | 68.200 | 0.837 |  0.098 | 0.698 | 2.807 | 19 |\n",
       "| Belgium                  | 6.834 | 10.823 | 0.906 | 72.199 | 0.783 | -0.153 | 0.646 | 2.862 | 20 |\n",
       "| France                   | 6.690 | 10.704 | 0.942 | 74.000 | 0.822 | -0.147 | 0.571 | 2.521 | 21 |\n",
       "| Bahrain                  | 6.647 | 10.669 | 0.862 | 69.495 | 0.925 |  0.089 | 0.722 | 2.631 | 22 |\n",
       "| Malta                    | 6.602 | 10.674 | 0.931 | 72.200 | 0.927 |  0.133 | 0.653 | 2.268 | 23 |\n",
       "| Taiwan Province of China | 6.584 | 10.871 | 0.898 | 69.600 | 0.784 | -0.070 | 0.721 | 2.687 | 24 |\n",
       "| United Arab Emirates     | 6.561 | 11.085 | 0.844 | 67.333 | 0.932 |  0.074 | 0.589 | 2.422 | 25 |\n",
       "| Saudi Arabia             | 6.494 | 10.743 | 0.891 | 66.603 | 0.877 | -0.149 | 0.684 | 2.668 | 26 |\n",
       "| Spain                    | 6.491 | 10.571 | 0.932 | 74.700 | 0.761 | -0.081 | 0.745 | 2.513 | 27 |\n",
       "| Italy                    | 6.483 | 10.623 | 0.880 | 73.800 | 0.693 | -0.084 | 0.866 | 2.794 | 28 |\n",
       "| Slovenia                 | 6.461 | 10.529 | 0.948 | 71.400 | 0.949 | -0.101 | 0.806 | 2.388 | 29 |\n",
       "| Guatemala                | 6.435 |  9.053 | 0.813 | 64.958 | 0.906 | -0.038 | 0.775 | 3.375 | 30 |\n",
       "| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |\n",
       "| Liberia                 | 4.625 | 7.288 | 0.720 | 56.498 | 0.735 |  0.050 | 0.850 | 2.857 | 120 |\n",
       "| Kenya                   | 4.607 | 8.361 | 0.688 | 60.704 | 0.779 |  0.287 | 0.825 | 2.180 | 121 |\n",
       "| Tunisia                 | 4.596 | 9.266 | 0.691 | 67.201 | 0.656 | -0.201 | 0.870 | 2.138 | 122 |\n",
       "| Lebanon                 | 4.584 | 9.626 | 0.848 | 67.355 | 0.525 | -0.073 | 0.898 | 1.736 | 123 |\n",
       "| Namibia                 | 4.574 | 9.161 | 0.818 | 56.799 | 0.719 | -0.149 | 0.847 | 2.068 | 124 |\n",
       "| Palestinian Territories | 4.517 | 8.485 | 0.826 | 62.250 | 0.653 | -0.163 | 0.821 | 2.131 | 125 |\n",
       "| Myanmar                 | 4.426 | 8.541 | 0.779 | 59.302 | 0.876 |  0.509 | 0.660 | 1.407 | 126 |\n",
       "| Jordan                  | 4.395 | 9.182 | 0.767 | 67.000 | 0.755 | -0.167 | 0.705 | 1.553 | 127 |\n",
       "| Chad                    | 4.355 | 7.364 | 0.619 | 48.478 | 0.579 |  0.041 | 0.807 | 3.209 | 128 |\n",
       "| Sri Lanka               | 4.325 | 9.470 | 0.827 | 67.299 | 0.841 |  0.079 | 0.863 | 1.075 | 129 |\n",
       "| Swaziland               | 4.308 | 9.065 | 0.770 | 50.833 | 0.647 | -0.185 | 0.708 | 2.155 | 130 |\n",
       "| Comoros                 | 4.289 | 8.031 | 0.626 | 57.349 | 0.548 |  0.082 | 0.781 | 2.610 | 131 |\n",
       "| Egypt                   | 4.283 | 9.367 | 0.750 | 61.998 | 0.749 | -0.182 | 0.795 | 1.648 | 132 |\n",
       "| Ethiopia                | 4.275 | 7.694 | 0.764 | 59.000 | 0.752 |  0.082 | 0.761 | 2.089 | 133 |\n",
       "| Mauritania              | 4.227 | 8.542 | 0.795 | 57.161 | 0.561 | -0.106 | 0.731 | 2.069 | 134 |\n",
       "| Madagascar              | 4.208 | 7.396 | 0.686 | 59.305 | 0.552 | -0.005 | 0.803 | 2.620 | 135 |\n",
       "| Togo                    | 4.107 | 7.362 | 0.569 | 54.914 | 0.619 |  0.032 | 0.772 | 2.806 | 136 |\n",
       "| Zambia                  | 4.073 | 8.145 | 0.708 | 55.809 | 0.782 |  0.061 | 0.823 | 1.975 | 137 |\n",
       "| Sierra Leone            | 3.849 | 7.434 | 0.630 | 51.651 | 0.717 |  0.084 | 0.866 | 2.396 | 138 |\n",
       "| India                   | 3.819 | 8.755 | 0.603 | 60.633 | 0.893 |  0.089 | 0.774 | 1.405 | 139 |\n",
       "| Burundi                 | 3.775 | 6.635 | 0.490 | 53.400 | 0.626 | -0.024 | 0.607 | 2.876 | 140 |\n",
       "| Yemen                   | 3.658 | 7.578 | 0.832 | 57.122 | 0.602 | -0.147 | 0.800 | 1.776 | 141 |\n",
       "| Tanzania                | 3.623 | 7.876 | 0.702 | 57.999 | 0.833 |  0.183 | 0.577 | 1.263 | 142 |\n",
       "| Haiti                   | 3.615 | 7.477 | 0.540 | 55.700 | 0.593 |  0.422 | 0.721 | 2.060 | 143 |\n",
       "| Malawi                  | 3.600 | 6.958 | 0.537 | 57.948 | 0.780 |  0.038 | 0.729 | 2.190 | 144 |\n",
       "| Lesotho                 | 3.512 | 7.926 | 0.787 | 48.700 | 0.715 | -0.131 | 0.915 | 1.800 | 145 |\n",
       "| Botswana                | 3.467 | 9.782 | 0.784 | 59.269 | 0.824 | -0.246 | 0.801 | 0.648 | 146 |\n",
       "| Rwanda                  | 3.415 | 7.676 | 0.552 | 61.400 | 0.897 |  0.061 | 0.167 | 1.095 | 147 |\n",
       "| Zimbabwe                | 3.145 | 7.943 | 0.750 | 56.201 | 0.677 | -0.047 | 0.821 | 1.205 | 148 |\n",
       "| Afghanistan             | 2.523 | 7.695 | 0.463 | 52.493 | 0.382 | -0.102 | 0.924 | 1.895 | 149 |\n",
       "\n"
      ],
      "text/plain": [
       "    Country                  Score GDP    Family Health Freedom Generosity\n",
       "1   Finland                  7.842 10.775 0.954  72.000 0.949   -0.098    \n",
       "2   Denmark                  7.620 10.933 0.954  72.700 0.946    0.030    \n",
       "3   Switzerland              7.571 11.117 0.942  74.400 0.919    0.025    \n",
       "4   Iceland                  7.554 10.878 0.983  73.000 0.955    0.160    \n",
       "5   Netherlands              7.464 10.932 0.942  72.400 0.913    0.175    \n",
       "6   Norway                   7.392 11.053 0.954  73.300 0.960    0.093    \n",
       "7   Sweden                   7.363 10.867 0.934  72.700 0.945    0.086    \n",
       "8   Luxembourg               7.324 11.647 0.908  72.600 0.907   -0.034    \n",
       "9   New Zealand              7.277 10.643 0.948  73.400 0.929    0.134    \n",
       "10  Austria                  7.268 10.906 0.934  73.300 0.908    0.042    \n",
       "11  Australia                7.183 10.796 0.940  73.900 0.914    0.159    \n",
       "12  Israel                   7.157 10.575 0.939  73.503 0.800    0.031    \n",
       "13  Germany                  7.155 10.873 0.903  72.500 0.875    0.011    \n",
       "14  Canada                   7.103 10.776 0.926  73.800 0.915    0.089    \n",
       "15  Ireland                  7.085 11.342 0.947  72.400 0.879    0.077    \n",
       "16  Costa Rica               7.069  9.880 0.891  71.400 0.934   -0.126    \n",
       "17  United Kingdom           7.064 10.707 0.934  72.500 0.859    0.233    \n",
       "18  Czech Republic           6.965 10.556 0.947  70.807 0.858   -0.208    \n",
       "19  United States            6.951 11.023 0.920  68.200 0.837    0.098    \n",
       "20  Belgium                  6.834 10.823 0.906  72.199 0.783   -0.153    \n",
       "21  France                   6.690 10.704 0.942  74.000 0.822   -0.147    \n",
       "22  Bahrain                  6.647 10.669 0.862  69.495 0.925    0.089    \n",
       "23  Malta                    6.602 10.674 0.931  72.200 0.927    0.133    \n",
       "24  Taiwan Province of China 6.584 10.871 0.898  69.600 0.784   -0.070    \n",
       "25  United Arab Emirates     6.561 11.085 0.844  67.333 0.932    0.074    \n",
       "26  Saudi Arabia             6.494 10.743 0.891  66.603 0.877   -0.149    \n",
       "27  Spain                    6.491 10.571 0.932  74.700 0.761   -0.081    \n",
       "28  Italy                    6.483 10.623 0.880  73.800 0.693   -0.084    \n",
       "29  Slovenia                 6.461 10.529 0.948  71.400 0.949   -0.101    \n",
       "30  Guatemala                6.435  9.053 0.813  64.958 0.906   -0.038    \n",
       "... ...                      ...   ...    ...    ...    ...     ...       \n",
       "120 Liberia                  4.625 7.288  0.720  56.498 0.735    0.050    \n",
       "121 Kenya                    4.607 8.361  0.688  60.704 0.779    0.287    \n",
       "122 Tunisia                  4.596 9.266  0.691  67.201 0.656   -0.201    \n",
       "123 Lebanon                  4.584 9.626  0.848  67.355 0.525   -0.073    \n",
       "124 Namibia                  4.574 9.161  0.818  56.799 0.719   -0.149    \n",
       "125 Palestinian Territories  4.517 8.485  0.826  62.250 0.653   -0.163    \n",
       "126 Myanmar                  4.426 8.541  0.779  59.302 0.876    0.509    \n",
       "127 Jordan                   4.395 9.182  0.767  67.000 0.755   -0.167    \n",
       "128 Chad                     4.355 7.364  0.619  48.478 0.579    0.041    \n",
       "129 Sri Lanka                4.325 9.470  0.827  67.299 0.841    0.079    \n",
       "130 Swaziland                4.308 9.065  0.770  50.833 0.647   -0.185    \n",
       "131 Comoros                  4.289 8.031  0.626  57.349 0.548    0.082    \n",
       "132 Egypt                    4.283 9.367  0.750  61.998 0.749   -0.182    \n",
       "133 Ethiopia                 4.275 7.694  0.764  59.000 0.752    0.082    \n",
       "134 Mauritania               4.227 8.542  0.795  57.161 0.561   -0.106    \n",
       "135 Madagascar               4.208 7.396  0.686  59.305 0.552   -0.005    \n",
       "136 Togo                     4.107 7.362  0.569  54.914 0.619    0.032    \n",
       "137 Zambia                   4.073 8.145  0.708  55.809 0.782    0.061    \n",
       "138 Sierra Leone             3.849 7.434  0.630  51.651 0.717    0.084    \n",
       "139 India                    3.819 8.755  0.603  60.633 0.893    0.089    \n",
       "140 Burundi                  3.775 6.635  0.490  53.400 0.626   -0.024    \n",
       "141 Yemen                    3.658 7.578  0.832  57.122 0.602   -0.147    \n",
       "142 Tanzania                 3.623 7.876  0.702  57.999 0.833    0.183    \n",
       "143 Haiti                    3.615 7.477  0.540  55.700 0.593    0.422    \n",
       "144 Malawi                   3.600 6.958  0.537  57.948 0.780    0.038    \n",
       "145 Lesotho                  3.512 7.926  0.787  48.700 0.715   -0.131    \n",
       "146 Botswana                 3.467 9.782  0.784  59.269 0.824   -0.246    \n",
       "147 Rwanda                   3.415 7.676  0.552  61.400 0.897    0.061    \n",
       "148 Zimbabwe                 3.145 7.943  0.750  56.201 0.677   -0.047    \n",
       "149 Afghanistan              2.523 7.695  0.463  52.493 0.382   -0.102    \n",
       "    Corruption Dystopia.residual Rank\n",
       "1   0.186      3.253              1  \n",
       "2   0.179      2.868              2  \n",
       "3   0.292      2.839              3  \n",
       "4   0.673      2.967              4  \n",
       "5   0.338      2.798              5  \n",
       "6   0.270      2.580              6  \n",
       "7   0.237      2.683              7  \n",
       "8   0.386      2.653              8  \n",
       "9   0.242      2.612              9  \n",
       "10  0.481      2.784             10  \n",
       "11  0.442      2.598             11  \n",
       "12  0.753      3.083             12  \n",
       "13  0.460      2.824             13  \n",
       "14  0.415      2.585             14  \n",
       "15  0.363      2.384             15  \n",
       "16  0.809      3.387             16  \n",
       "17  0.459      2.596             17  \n",
       "18  0.868      3.124             18  \n",
       "19  0.698      2.807             19  \n",
       "20  0.646      2.862             20  \n",
       "21  0.571      2.521             21  \n",
       "22  0.722      2.631             22  \n",
       "23  0.653      2.268             23  \n",
       "24  0.721      2.687             24  \n",
       "25  0.589      2.422             25  \n",
       "26  0.684      2.668             26  \n",
       "27  0.745      2.513             27  \n",
       "28  0.866      2.794             28  \n",
       "29  0.806      2.388             29  \n",
       "30  0.775      3.375             30  \n",
       "... ...        ...               ... \n",
       "120 0.850      2.857             120 \n",
       "121 0.825      2.180             121 \n",
       "122 0.870      2.138             122 \n",
       "123 0.898      1.736             123 \n",
       "124 0.847      2.068             124 \n",
       "125 0.821      2.131             125 \n",
       "126 0.660      1.407             126 \n",
       "127 0.705      1.553             127 \n",
       "128 0.807      3.209             128 \n",
       "129 0.863      1.075             129 \n",
       "130 0.708      2.155             130 \n",
       "131 0.781      2.610             131 \n",
       "132 0.795      1.648             132 \n",
       "133 0.761      2.089             133 \n",
       "134 0.731      2.069             134 \n",
       "135 0.803      2.620             135 \n",
       "136 0.772      2.806             136 \n",
       "137 0.823      1.975             137 \n",
       "138 0.866      2.396             138 \n",
       "139 0.774      1.405             139 \n",
       "140 0.607      2.876             140 \n",
       "141 0.800      1.776             141 \n",
       "142 0.577      1.263             142 \n",
       "143 0.721      2.060             143 \n",
       "144 0.729      2.190             144 \n",
       "145 0.915      1.800             145 \n",
       "146 0.801      0.648             146 \n",
       "147 0.167      1.095             147 \n",
       "148 0.821      1.205             148 \n",
       "149 0.924      1.895             149 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Adding country ranks in the dataset according to their scores\n",
    "happ <- happ %>% mutate(Rank = row_number())\n",
    "happ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Correlation matrix </b> <br>\n",
    "To find out which factors correlate the most with the happiness quotient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t149 obs. of  10 variables:\n",
      " $ Country          : chr  \"Finland\" \"Denmark\" \"Switzerland\" \"Iceland\" ...\n",
      " $ Score            : num  7.84 7.62 7.57 7.55 7.46 ...\n",
      " $ GDP              : num  10.8 10.9 11.1 10.9 10.9 ...\n",
      " $ Family           : num  0.954 0.954 0.942 0.983 0.942 0.954 0.934 0.908 0.948 0.934 ...\n",
      " $ Health           : num  72 72.7 74.4 73 72.4 73.3 72.7 72.6 73.4 73.3 ...\n",
      " $ Freedom          : num  0.949 0.946 0.919 0.955 0.913 0.96 0.945 0.907 0.929 0.908 ...\n",
      " $ Generosity       : num  -0.098 0.03 0.025 0.16 0.175 0.093 0.086 -0.034 0.134 0.042 ...\n",
      " $ Corruption       : num  0.186 0.179 0.292 0.673 0.338 0.27 0.237 0.386 0.242 0.481 ...\n",
      " $ Dystopia.residual: num  3.25 2.87 2.84 2.97 2.8 ...\n",
      " $ Rank             : int  1 2 3 4 5 6 7 8 9 10 ...\n",
      "                        Score         GDP      Family     Health    Freedom\n",
      "Score              1.00000000  0.78975970  0.75688765  0.7680995  0.6077531\n",
      "GDP                0.78975970  1.00000000  0.78529868  0.8594606  0.4323235\n",
      "Family             0.75688765  0.78529868  1.00000000  0.7232561  0.4829298\n",
      "Health             0.76809946  0.85946062  0.72325613  1.0000000  0.4614939\n",
      "Freedom            0.60775307  0.43232349  0.48292977  0.4614939  1.0000000\n",
      "Generosity        -0.01779928 -0.19928640 -0.11494585 -0.1617503  0.1694374\n",
      "Corruption        -0.42114000 -0.34233742 -0.20320698 -0.3643735 -0.4013630\n",
      "Dystopia.residual  0.49200990 -0.04111918  0.00668051 -0.0234329  0.1002423\n",
      "Rank              -0.98426526 -0.79698747 -0.75102401 -0.7759416 -0.6064113\n",
      "                   Generosity  Corruption Dystopia.residual        Rank\n",
      "Score             -0.01779928 -0.42114000        0.49200990 -0.98426526\n",
      "GDP               -0.19928640 -0.34233742       -0.04111918 -0.79698747\n",
      "Family            -0.11494585 -0.20320698        0.00668051 -0.75102401\n",
      "Health            -0.16175028 -0.36437353       -0.02343290 -0.77594156\n",
      "Freedom            0.16943737 -0.40136297        0.10024227 -0.60641135\n",
      "Generosity         1.00000000 -0.16396173       -0.02721188  0.03966234\n",
      "Corruption        -0.16396173  1.00000000        0.00447841  0.40065425\n",
      "Dystopia.residual -0.02721188  0.00447841        1.00000000 -0.46354759\n",
      "Rank               0.03966234  0.40065425       -0.46354759  1.00000000\n"
     ]
    },
    {
     "ename": "ERROR",
     "evalue": "Error in cor(happ): 'x' must be numeric\n",
     "output_type": "error",
     "traceback": [
      "Error in cor(happ): 'x' must be numeric\nTraceback:\n",
      "1. cor(happ)",
      "2. stop(\"'x' must be numeric\")"
     ]
    }
   ],
   "source": [
    "library(corrgram) \n",
    "library(corrplot)\n",
    "\n",
    "library(rpart) \n",
    "str(happ)\n",
    "\n",
    "library(ggplot2) \n",
    "library(ggthemes) \n",
    "num.cols <- sapply(happ,is.numeric) \n",
    "cor.data <- cor(happ[,num.cols]) \n",
    "print(cor.data)\n",
    "\n",
    "#correlation matrix\n",
    "cor <- cor(happ)\n",
    "\n",
    "str(happ)\n",
    "colnames(happ)\n",
    "head(happ) \n",
    "summary(happ)\n",
    "print(corrplot(cor.data, method = 'color')) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(corrgram) \n",
    "library(corrplot)\n",
    "\n",
    "print(corrplot(cor.data, method = 'color')) \n",
    "ggsave(\"corr.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hence, happiness most strongly correlates with the attributes of GDP, Health, Social Support(Family) and Freedom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t149 obs. of  11 variables:\n",
      " $ Country          : chr  \"Finland\" \"Denmark\" \"Switzerland\" \"Iceland\" ...\n",
      " $ Continent        : Factor w/ 5 levels \"Africa\",\"Asia\",..: 3 3 3 3 3 3 3 3 1 3 ...\n",
      " $ Score            : num  7.84 7.62 7.57 7.55 7.46 ...\n",
      " $ GDP              : num  10.8 10.9 11.1 10.9 10.9 ...\n",
      " $ Family           : num  0.954 0.954 0.942 0.983 0.942 0.954 0.934 0.908 0.948 0.934 ...\n",
      " $ Health           : num  72 72.7 74.4 73 72.4 73.3 72.7 72.6 73.4 73.3 ...\n",
      " $ Freedom          : num  0.949 0.946 0.919 0.955 0.913 0.96 0.945 0.907 0.929 0.908 ...\n",
      " $ Generosity       : num  -0.098 0.03 0.025 0.16 0.175 0.093 0.086 -0.034 0.134 0.042 ...\n",
      " $ Corruption       : num  0.186 0.179 0.292 0.673 0.338 0.27 0.237 0.386 0.242 0.481 ...\n",
      " $ Dystopia.residual: num  3.25 2.87 2.84 2.97 2.8 ...\n",
      " $ Rank             : int  1 2 3 4 5 6 7 8 9 10 ...\n"
     ]
    }
   ],
   "source": [
    "# Creating a new column for continents\n",
    "\n",
    "happ$Continent <- NA\n",
    "\n",
    "happ$Continent[which(happ$Country %in% c(\"Israel\", \"United Arab Emirates\", \"Singapore\", \"Thailand\", \"Taiwan Province of China\",\n",
    "                                   \"Qatar\", \"Saudi Arabia\", \"Kuwait\", \"Bahrain\", \"Malaysia\", \"Uzbekistan\", \"Japan\",\n",
    "                                   \"South Korea\", \"Turkmenistan\", \"Kazakhstan\", \"Turkey\", \"Hong Kong S.A.R., China\", \"Philippines\",\n",
    "                                   \"Jordan\", \"China\", \"Pakistan\", \"Indonesia\", \"Azerbaijan\", \"Lebanon\", \"Vietnam\",\n",
    "                                   \"Tajikistan\", \"Bhutan\", \"Kyrgyzstan\", \"Nepal\", \"Mongolia\", \"Palestinian Territories\",\n",
    "                                   \"Iran\", \"Bangladesh\", \"Myanmar\", \"Iraq\", \"Sri Lanka\", \"Armenia\", \"India\", \"Georgia\",\n",
    "                                   \"Cambodia\", \"Afghanistan\", \"Yemen\", \"Syria\"))] <- \"Asia\"\n",
    "happ$Continent[which(happ$Country %in% c(\"Norway\", \"Denmark\", \"Iceland\", \"Switzerland\", \"Finland\",\n",
    "                                   \"Netherlands\", \"Sweden\", \"Austria\", \"Ireland\", \"Germany\",\n",
    "                                   \"Belgium\", \"Luxembourg\", \"United Kingdom\", \"Czech Republic\",\n",
    "                                   \"Malta\", \"France\", \"Spain\", \"Slovakia\", \"Poland\", \"Italy\",\n",
    "                                   \"Russia\", \"Lithuania\", \"Latvia\", \"Moldova\", \"Romania\",\n",
    "                                   \"Slovenia\", \"North Cyprus\", \"Cyprus\", \"Estonia\", \"Belarus\",\n",
    "                                   \"Serbia\", \"Hungary\", \"Croatia\", \"Kosovo\", \"Montenegro\",\n",
    "                                   \"Greece\", \"Portugal\", \"Bosnia and Herzegovina\", \"Macedonia\",\n",
    "                                   \"Bulgaria\", \"Albania\", \"Ukraine\"))] <- \"Europe\"\n",
    "happ$Continent[which(happ$Country %in% c(\"Canada\", \"Costa Rica\", \"United States\", \"Mexico\",  \n",
    "                                   \"Panama\",\"Trinidad and Tobago\", \"El Salvador\", \"Belize\", \"Guatemala\",\n",
    "                                   \"Jamaica\", \"Nicaragua\", \"Dominican Republic\", \"Honduras\",\n",
    "                                   \"Haiti\"))] <- \"North America\"\n",
    "happ$Continent[which(happ$Country %in% c(\"Chile\", \"Brazil\", \"Argentina\", \"Uruguay\",\n",
    "                                   \"Colombia\", \"Ecuador\", \"Bolivia\", \"Peru\",\n",
    "                                   \"Paraguay\", \"Venezuela\"))] <- \"South America\"\n",
    "#happ$Continent[which(happ$Country %in% c(\"New Zealand\", \"Australia\"))] <- \"Australia\"\n",
    "happ$Continent[which(is.na(happ$Continent))] <- \"Africa\"\n",
    "\n",
    "\n",
    "# Moving the continent column's position in the dataset to the second column\n",
    "\n",
    "happ <- happ %>% select(Country,Continent, everything())\n",
    "\n",
    "# Changing Continent column to factor\n",
    "\n",
    "happ$Continent <- as.factor(happ$Continent)\n",
    "\n",
    "str(happ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Country</th><th scope=col>Continent</th><th scope=col>Score</th><th scope=col>GDP</th><th scope=col>Family</th><th scope=col>Health</th><th scope=col>Freedom</th><th scope=col>Generosity</th><th scope=col>Corruption</th><th scope=col>Dystopia.residual</th><th scope=col>Rank</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Finland    </td><td>Europe</td><td>7.842</td><td>10.775</td><td>0.954</td><td>72.0</td><td>0.949</td><td>-0.098</td><td>0.186</td><td>3.253</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Denmark    </td><td>Europe</td><td>7.620</td><td>10.933</td><td>0.954</td><td>72.7</td><td>0.946</td><td> 0.030</td><td>0.179</td><td>2.868</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Switzerland</td><td>Europe</td><td>7.571</td><td>11.117</td><td>0.942</td><td>74.4</td><td>0.919</td><td> 0.025</td><td>0.292</td><td>2.839</td><td>3</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Iceland    </td><td>Europe</td><td>7.554</td><td>10.878</td><td>0.983</td><td>73.0</td><td>0.955</td><td> 0.160</td><td>0.673</td><td>2.967</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Netherlands</td><td>Europe</td><td>7.464</td><td>10.932</td><td>0.942</td><td>72.4</td><td>0.913</td><td> 0.175</td><td>0.338</td><td>2.798</td><td>5</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Norway     </td><td>Europe</td><td>7.392</td><td>11.053</td><td>0.954</td><td>73.3</td><td>0.960</td><td> 0.093</td><td>0.270</td><td>2.580</td><td>6</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & Country & Continent & Score & GDP & Family & Health & Freedom & Generosity & Corruption & Dystopia.residual & Rank\\\\\n",
       "  & <chr> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & Finland     & Europe & 7.842 & 10.775 & 0.954 & 72.0 & 0.949 & -0.098 & 0.186 & 3.253 & 1\\\\\n",
       "\t2 & Denmark     & Europe & 7.620 & 10.933 & 0.954 & 72.7 & 0.946 &  0.030 & 0.179 & 2.868 & 2\\\\\n",
       "\t3 & Switzerland & Europe & 7.571 & 11.117 & 0.942 & 74.4 & 0.919 &  0.025 & 0.292 & 2.839 & 3\\\\\n",
       "\t4 & Iceland     & Europe & 7.554 & 10.878 & 0.983 & 73.0 & 0.955 &  0.160 & 0.673 & 2.967 & 4\\\\\n",
       "\t5 & Netherlands & Europe & 7.464 & 10.932 & 0.942 & 72.4 & 0.913 &  0.175 & 0.338 & 2.798 & 5\\\\\n",
       "\t6 & Norway      & Europe & 7.392 & 11.053 & 0.954 & 73.3 & 0.960 &  0.093 & 0.270 & 2.580 & 6\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 11\n",
       "\n",
       "| <!--/--> | Country &lt;chr&gt; | Continent &lt;fct&gt; | Score &lt;dbl&gt; | GDP &lt;dbl&gt; | Family &lt;dbl&gt; | Health &lt;dbl&gt; | Freedom &lt;dbl&gt; | Generosity &lt;dbl&gt; | Corruption &lt;dbl&gt; | Dystopia.residual &lt;dbl&gt; | Rank &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | Finland     | Europe | 7.842 | 10.775 | 0.954 | 72.0 | 0.949 | -0.098 | 0.186 | 3.253 | 1 |\n",
       "| 2 | Denmark     | Europe | 7.620 | 10.933 | 0.954 | 72.7 | 0.946 |  0.030 | 0.179 | 2.868 | 2 |\n",
       "| 3 | Switzerland | Europe | 7.571 | 11.117 | 0.942 | 74.4 | 0.919 |  0.025 | 0.292 | 2.839 | 3 |\n",
       "| 4 | Iceland     | Europe | 7.554 | 10.878 | 0.983 | 73.0 | 0.955 |  0.160 | 0.673 | 2.967 | 4 |\n",
       "| 5 | Netherlands | Europe | 7.464 | 10.932 | 0.942 | 72.4 | 0.913 |  0.175 | 0.338 | 2.798 | 5 |\n",
       "| 6 | Norway      | Europe | 7.392 | 11.053 | 0.954 | 73.3 | 0.960 |  0.093 | 0.270 | 2.580 | 6 |\n",
       "\n"
      ],
      "text/plain": [
       "  Country     Continent Score GDP    Family Health Freedom Generosity\n",
       "1 Finland     Europe    7.842 10.775 0.954  72.0   0.949   -0.098    \n",
       "2 Denmark     Europe    7.620 10.933 0.954  72.7   0.946    0.030    \n",
       "3 Switzerland Europe    7.571 11.117 0.942  74.4   0.919    0.025    \n",
       "4 Iceland     Europe    7.554 10.878 0.983  73.0   0.955    0.160    \n",
       "5 Netherlands Europe    7.464 10.932 0.942  72.4   0.913    0.175    \n",
       "6 Norway      Europe    7.392 11.053 0.954  73.3   0.960    0.093    \n",
       "  Corruption Dystopia.residual Rank\n",
       "1 0.186      3.253             1   \n",
       "2 0.179      2.868             2   \n",
       "3 0.292      2.839             3   \n",
       "4 0.673      2.967             4   \n",
       "5 0.338      2.798             5   \n",
       "6 0.270      2.580             6   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(happ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Finding the average value of attributes per continent </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"`funs()` was deprecated in dplyr 0.8.0.\n",
      "Please use a list of either functions or lambdas: \n",
      "\n",
      "  # Simple named list: \n",
      "  list(mean = mean, median = median)\n",
      "\n",
      "  # Auto named with `tibble::lst()`: \n",
      "  tibble::lst(mean, median)\n",
      "\n",
      "  # Using lambdas\n",
      "  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))\n",
      "\u001b[90mThis warning is displayed once every 8 hours.\u001b[39m\n",
      "\u001b[90mCall `lifecycle::last_warnings()` to see where this warning was generated.\u001b[39m\"\n",
      "Warning message in melt(happ.Continent):\n",
      "\"The melt generic in data.table has been passed a tbl_df and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(happ.Continent). In the next version, this warning will become an error.\"\n",
      "Using Continent as id variables\n",
      "\n",
      "Saving 6.67 x 6.67 in image\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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NsRwtkVQhilEEIhFMJ2hHB2hRBGKYRQCIWwHSGcXSGEUQohFEIhbEcIZ1cIYZRC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\nYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=15, repr.plot.height=15)\n",
    "happ.Continent <- happ %>%\n",
    "                          select(-3) %>%\n",
    "                          group_by(Continent) %>%\n",
    "                          summarise_at(vars(-Country), funs(mean(., na.rm=TRUE)))\n",
    "\n",
    "happ.Continent.melt <- melt(happ.Continent)\n",
    "\n",
    "# Faceting\n",
    "ggplot(happ.Continent.melt, aes(y=value, x=Continent, color=Continent, fill=Continent)) + \n",
    "  geom_bar( stat=\"identity\") +    \n",
    "  facet_wrap(~variable) + theme_bw() +\n",
    "  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +\n",
    "  labs(title = \"Average value of happiness variables for different continents\", \n",
    "       y = \"Average value\") \n",
    "ggsave(\"avgcont.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Finding how attributes correlate to happiness in the continent of Asia. </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Checking happiness matrix for Asia\n",
    "options(repr.plot.width=10, repr.plot.height=8)\n",
    "corrgram(happ %>% select(-3) %>% filter(Continent == \"Asia\"), order=TRUE,\n",
    "         upper.panel=panel.cor, main=\"Happiness Matrix for Asia\")\n",
    "ggsave(\"asiacorr.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Happiness score distribution across different continents using a scatter plot </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=8)\n",
    "gg1 <- ggplot(happ,\n",
    "              aes(x=Continent,\n",
    "                  y=Score,\n",
    "                  color=Continent))+\n",
    "  geom_point() + theme_bw() +\n",
    "  theme(axis.title = element_text(family = \"Helvetica\", size = (8)))\n",
    "gg1\n",
    "ggsave(\"happcont.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Prediction </b> <br>\n",
    "Splitting dataset to training and testing datasets and using Linear Regression to predict the dependent variable for a country (Happiness Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(caTools)\n",
    "set.seed(123)\n",
    "dataset <- happ[3:10] #relevant columns into training\n",
    "split = sample.split(dataset$Score, SplitRatio = 0.7) #splitting\n",
    "training_set = subset(dataset, split == TRUE) #training set\n",
    "test_set = subset(dataset, split == FALSE) #test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = Score ~ ., data = training_set)\n",
       "\n",
       "Residuals:\n",
       "       Min         1Q     Median         3Q        Max \n",
       "-0.0019526 -0.0005332 -0.0001061  0.0005323  0.0020082 \n",
       "\n",
       "Coefficients:\n",
       "                    Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)       -4.563e+00  1.203e-03   -3792   <2e-16 ***\n",
       "GDP                3.494e-01  1.557e-04    2244   <2e-16 ***\n",
       "Family             2.252e+00  1.232e-03    1828   <2e-16 ***\n",
       "Health             3.147e-02  2.359e-05    1334   <2e-16 ***\n",
       "Freedom            1.219e+00  9.658e-04    1262   <2e-16 ***\n",
       "Generosity         6.518e-01  5.931e-04    1099   <2e-16 ***\n",
       "Corruption        -6.386e-01  5.500e-04   -1161   <2e-16 ***\n",
       "Dystopia.residual  9.999e-01  1.530e-04    6535   <2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 0.0008327 on 96 degrees of freedom\n",
       "Multiple R-squared:      1,\tAdjusted R-squared:      1 \n",
       "F-statistic: 2.448e+07 on 7 and 96 DF,  p-value: < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "regressor_lm = lm(formula = Score ~ .,\n",
    "               data = training_set)\n",
    "\n",
    "summary(regressor_lm) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An adjusted R-squared of 1 is obtained. The Multiple Linear Regression predicts happiness scores with an accuracy of 100%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "Saving 6.67 x 6.67 in image\n",
      "\n",
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      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
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      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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mEDBuDiCwygGUD7Ce6gQ9iAAbj4AgNo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\nZNcawiTJrv0fRIJZmjK/Z30AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_lm = predict(regressor_lm, newdata = test_set)\n",
    "\n",
    "Pred_Actual_lm <- as.data.frame(cbind(Prediction = y_pred_lm, Actual = test_set$Score))\n",
    "\n",
    "gg.lm <- ggplot(Pred_Actual_lm, aes(Actual, Prediction )) +\n",
    "  geom_point() + theme_bw() + geom_abline() +\n",
    "  labs(title = \"Multiple Linear Regression\", x = \"Actual happiness score\",\n",
    "       y = \"Predicted happiness score\") +\n",
    "  theme(plot.title = element_text(family = \"Helvetica\", face = \"bold\", size = (15)), \n",
    "        axis.title = element_text(family = \"Helvetica\", size = (10))) \n",
    "\n",
    "gg.lm\n",
    "ggsave(\"model.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the above graph shows the accuracy of our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "MSE.lm <- sum((test_set$Happiness.Score - y_pred_lm)^2)/nrow(test_set)\n",
    "print(paste(\"Mean Squared Error (Multiple Linear Regression):\", MSE.lm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Providing a test input to the model to predict a happiness score </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test1 <- data.frame(GDP = 10.555,\n",
    "                  Family = 0.867,\n",
    "                  Health = 71.02,\n",
    "                  Freedom = 0.923,\n",
    "                  Generosity = 0.021,\n",
    "                  Corruption = 0.723,\n",
    "                  Dystopia.residual = 2.463)\n",
    "print(paste(\"Happiness Score: \", round(predict(regressor_lm, test1), 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On providing the above test inputs, the model predicts this set's Happiness Score to be 6.45 which would put this sample country somewhere in the top 30 happiest countries in the current scenario. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Checking the trend of each attribute basis continent </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=12)\n",
    "\n",
    "ggplot(data = happ, mapping=aes(x = Rank, y = GDP, color=Score)) +\n",
    "    geom_line()+facet_wrap(facets =  vars(Continent))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ggplot(data = happ, mapping=aes(x = Rank, y = Family, color=Score)) +\n",
    "    geom_line()+facet_wrap(facets =  vars(Continent))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ggplot(data = happ, mapping=aes(x = Rank, y = Health, color=Score)) +\n",
    "    geom_line()+facet_wrap(facets =  vars(Continent)) + ggtitle(\"Health index over the continents\")\n",
    "ggsave(\"health.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ggplot(data = happ, mapping=aes(x = Rank, y = Freedom, color=Score)) +\n",
    "    geom_line()+facet_wrap(facets =  vars(Continent))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ggplot(data = happ, mapping=aes(x = Rank, y = Generosity, color=Score)) +\n",
    "    geom_line()+facet_wrap(facets =  vars(Continent))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ggplot(data = happ, mapping=aes(x = Rank, y = Corruption, color=Score)) +\n",
    "    geom_line()+facet_wrap(facets =  vars(Continent))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ggplot(data = happ, mapping=aes(x = Rank, y = Dystopia.residual, color=Score)) +\n",
    "    geom_line()+facet_wrap(facets =  vars(Continent))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Checking the top 10 and bottom 10 countries on the World Happiness Report for 2021 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "options(repr.plot.width=8, repr.plot.height=6)\n",
    "\n",
    "ggplot(happ[1:10,], aes(x = reorder(Country, Score), y=Score, fill = Country)) +  ggtitle(\"Top 10 happiest countries in 2021\", subtitle = \"9 of the happiest countries are in Europe\") + geom_bar(stat=\"identity\", width=0.7) + theme(plot.title = element_text(size=20)) + coord_flip()\n",
    "ggsave(\"top10.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ggplot(happ[140:149,], aes(x = reorder(Country, -Score), y=Score, fill = Country)) + ggtitle(\"Top 10 saddest countries in 2021\") + geom_bar(stat=\"identity\", width=0.7) + theme(plot.title = element_text(size=20)) + coord_flip()\n",
    "ggsave(\"bottom10.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "happ_all <- read.csv(\"world-happiness-report.csv\") #importing the dataset containing happiness scores from previous years country-wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dim(happ_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "happ_copy <- read.csv(\"world-happiness-report-2021.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_region_dict = happ_copy %>% select(country = ï..Country.name, region = Regional.indicator) %>% unique()\n",
    "\n",
    "df_happy_increase <- happ_all %>% \n",
    "    filter(year >= 2018) %>%\n",
    "    left_join(country_region_dict, by = c('ï..Country.name' = 'country')) %>%\n",
    "    select(country = ï..Country.name, year, ladder = Life.Ladder)  %>%\n",
    "    pivot_wider(names_from = 'year', names_prefix = 'year', values_from = 'ladder') %>%\n",
    "    mutate(increase_in_2019 = ifelse(year2019>year2018, 1, 0),\n",
    "          increase_in_2020 = ifelse(year2020>year2019,1,0))\n",
    "\n",
    "df_increase_in_2019 <- df_happy_increase %>% summarize(pct = mean(increase_in_2019, na.rm = TRUE))\n",
    "df_increase_in_2020 <- df_happy_increase %>% summarize(pct = mean(increase_in_2020, na.rm = TRUE))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Comparing the increase in Happiness coefficients Pre-Pandemic and Amidst-Pandemic </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(ggpubr)\n",
    "donut_plot <- function(df, title = '', subtitle = '', caption = '') {\n",
    "ggplot(df) +\n",
    "    geom_rect(aes(ymax = 1, ymin = 0, xmax = 2, xmin=1.2, fill = \"base\"))  +\n",
    "    geom_rect(aes(ymax = pct, ymin = 0, xmax = 2.2, xmin = 1.2, fill = 'main')) +\n",
    "    geom_text(x = 0, y = 0, label = paste0(round(df$pct*100,0),'%'), size = 16) + \n",
    "    coord_polar(theta = 'y') +\n",
    "    xlim(c(0,2.2)) + \n",
    "    scale_fill_manual(values = c(\"#e3e2e1\", \"#7FB185\")) +\n",
    "    labs(title = title, subtitle = subtitle, caption = caption) +\n",
    "    theme_void() + \n",
    "    theme(plot.title = element_text(size=22, face=\"bold\"),\n",
    "          plot.subtitle = element_text(size = 20, hjust = 0.5),\n",
    "          plot.caption = element_text(size = 15),\n",
    "          legend.position = 'None')}\n",
    "\n",
    "p1 <- donut_plot(df_increase_in_2019, title = 'Percent of countries with increased happiness\\n Pre-Covid(2018 - 2019) and Amidst-Covid(2019-2020)\\n', subtitle = '2018 -> 2019')\n",
    "p2 <- donut_plot(df_increase_in_2020, title = '\\n', subtitle = '2019 -> 2020')\n",
    "figure <- ggarrange(p1, p2, ncol = 2, nrow = 1)\n",
    "figure\n",
    "ggsave(\"covidcompare.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cor <- happ_copy %>% \n",
    "    select(corruption = Perceptions.of.corruption,\n",
    "           generosity = Generosity,\n",
    "           freedom = Freedom.to.make.life.choices, \n",
    "           life_expectancy = Healthy.life.expectancy, \n",
    "           social_support = Social.support,\n",
    "           GDP_per_capita = Logged.GDP.per.capita, \n",
    "           happiness = Ladder.score\n",
    "           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(rwa)\n",
    "predictors = colnames(df_cor)[1:6]\n",
    "outcome = 'happiness'\n",
    "rwa <- rwa(df_cor,\n",
    "    outcome = outcome,\n",
    "    predictors = predictors,\n",
    "    applysigns = FALSE) \n",
    "\n",
    "rsquare <- rwa$rsquare\n",
    "(relative_weight <- rwa$result)\n",
    "n <- rwa$n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ggplot(relative_weight, aes(x = reorder(Variables, Rescaled.RelWeight), y=Rescaled.RelWeight, fill = Variables)) +  ggtitle(\"Top important factors affecting the \\n happiness index in 2021\", subtitle = \"1. GDP 2. Social Support 3. Health\") + geom_bar(stat=\"identity\", width=0.7) + theme(plot.title = element_text(size=20), plot.subtitle = element_text(size=15)) + coord_flip()\n",
    "ggsave(\"topfactors.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "head(happ_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Performing some visualizations on data from India </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "happ_ind=happ_all[happ_all$ï..Country.name == \"India\", ]  \n",
    "happ_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ggplot(data = happ_ind, mapping=aes(x = year, y = Life.Ladder, color=Life.Ladder)) + geom_line() + ggtitle(\"Happiness Score of India over the last 15 years\") + theme(plot.title = element_text(size=20))\n",
    "ggsave(\"indiahappy.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ggplot(data = happ_ind, mapping=aes(x = year, y = Log.GDP.per.capita, color=Log.GDP.per.capita)) + geom_line() + ggtitle(\"GDP Score of India over the last 15 years\") + theme(plot.title = element_text(size=20))\n",
    "ggsave(\"indiagdp.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ggplot(data = happ_ind, mapping=aes(x = year, y = Perceptions.of.corruption, color=Perceptions.of.corruption)) + geom_line() + ggtitle(\"Perceptions of Corruption in India over the last 15 years\") + theme(plot.title = element_text(size=20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ggplot(data = happ_ind, mapping=aes(x = year, y = Healthy.life.expectancy.at.birth, color=Healthy.life.expectancy.at.birth)) + geom_line() + ggtitle(\"Health infrastructure in India over the last 15 years\") + theme(plot.title = element_text(size=20))\n",
    "ggsave(\"indiahealth.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "view(happ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "view(happ_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean(happ$Ladder.score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "median(happ$Healthy.life.expectancy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sd(happ$Logged.GDP.per.capita)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "head(happ_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "max(happ_all$Life.Ladder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_country=happ_all[happ_all$Life.Ladder == min(happ_all$Life.Ladder), ]  \n",
    "min_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_country=happ_all[happ_all$Life.Ladder == max(happ_all$Life.Ladder), ]  \n",
    "max_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "svm(formula = Score ~ ., data = dataset, type = \"eps-regression\", \n",
       "    kernel = \"radial\")\n",
       "\n",
       "\n",
       "Parameters:\n",
       "   SVM-Type:  eps-regression \n",
       " SVM-Kernel:  radial \n",
       "       cost:  1 \n",
       "      gamma:  0.1428571 \n",
       "    epsilon:  0.1 \n",
       "\n",
       "\n",
       "Number of Support Vectors:  55\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(e1071)\n",
    "regressor_svr = svm(formula = Score ~ .,\n",
    "                data = dataset,\n",
    "                type = 'eps-regression',\n",
    "                kernel = 'radial')\n",
    "summary(regressor_svr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
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      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
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      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
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      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Saving 6.67 x 6.67 in image\n",
      "\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n"
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    },
    {
     "data": {
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gCuvsAAmgEL7bWDDuGwR4IYgKsvMIBm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\nSUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_svr = predict(regressor_svr,  newdata = test_set)\n",
    "\n",
    "Pred_Actual_svr <- as.data.frame(cbind(Prediction = y_pred_svr, Actual = test_set$Score))\n",
    "\n",
    "\n",
    "Pred_Actual_lm.versus.svr <- cbind(Prediction.lm = y_pred_lm, Prediction.svr = y_pred_svr, Actual = test_set$Score)\n",
    "\n",
    "gg.svr <- ggplot(Pred_Actual_svr, aes(Actual, Prediction)) +\n",
    "  geom_point() + theme_bw() + geom_abline() +\n",
    "  labs(title = \"SVR\", x = \"Actual happiness score\",\n",
    "       y = \"Predicted happiness score\") +\n",
    "  theme(plot.title = element_text(family = \"Helvetica\", face = \"bold\", size = (15)), \n",
    "        axis.title = element_text(family = \"Helvetica\", size = (10)))\n",
    "gg.svr\n",
    "ggsave(\"svrmodel.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Call:\n",
      "rpart(formula = Score ~ ., data = dataset, control = rpart.control(minsplit = 10))\n",
      "  n= 149 \n",
      "\n",
      "          CP nsplit rel error    xerror       xstd\n",
      "1 0.50721060      0 1.0000000 1.0101114 0.10544011\n",
      "2 0.14571618      1 0.4927894 0.6157971 0.05505040\n",
      "3 0.07771100      2 0.3470732 0.4384032 0.04151696\n",
      "4 0.05496191      3 0.2693622 0.3876167 0.04027925\n",
      "5 0.04847301      4 0.2144003 0.3622854 0.03929269\n",
      "6 0.02546646      5 0.1659273 0.2878238 0.03649581\n",
      "7 0.01444021      6 0.1404608 0.2626825 0.03105171\n",
      "8 0.01424894      7 0.1260206 0.2549639 0.02929547\n",
      "9 0.01000000      8 0.1117717 0.2538658 0.02818508\n",
      "\n",
      "Variable importance\n",
      "              GDP            Health            Family           Freedom \n",
      "               27                22                19                10 \n",
      "       Corruption Dystopia.residual        Generosity \n",
      "                9                 8                 4 \n",
      "\n",
      "Node number 1: 149 observations,    complexity param=0.5072106\n",
      "  mean=5.532839, MSE=1.145571 \n",
      "  left son=2 (91 obs) right son=3 (58 obs)\n",
      "  Primary splits:\n",
      "      GDP               < 9.856   to the left,  improve=0.5072106, (0 missing)\n",
      "      Family            < 0.8615  to the left,  improve=0.4924233, (0 missing)\n",
      "      Health            < 67.2995 to the left,  improve=0.4899874, (0 missing)\n",
      "      Dystopia.residual < 2.2075  to the left,  improve=0.3224869, (0 missing)\n",
      "      Freedom           < 0.7825  to the left,  improve=0.2656577, (0 missing)\n",
      "  Surrogate splits:\n",
      "      Health     < 68.149  to the left,  agree=0.872, adj=0.672, (0 split)\n",
      "      Family     < 0.878   to the left,  agree=0.846, adj=0.603, (0 split)\n",
      "      Corruption < 0.654   to the right, agree=0.718, adj=0.276, (0 split)\n",
      "      Freedom    < 0.9065  to the left,  agree=0.698, adj=0.224, (0 split)\n",
      "      Generosity < -0.252  to the right, agree=0.624, adj=0.034, (0 split)\n",
      "\n",
      "Node number 2: 91 observations,    complexity param=0.1457162\n",
      "  mean=4.924286, MSE=0.637727 \n",
      "  left son=4 (44 obs) right son=5 (47 obs)\n",
      "  Primary splits:\n",
      "      Health            < 61.999  to the left,  improve=0.4285881, (0 missing)\n",
      "      Family            < 0.797   to the left,  improve=0.3589446, (0 missing)\n",
      "      GDP               < 8.059   to the left,  improve=0.3464340, (0 missing)\n",
      "      Dystopia.residual < 2.1905  to the left,  improve=0.2976079, (0 missing)\n",
      "      Freedom           < 0.784   to the left,  improve=0.1917278, (0 missing)\n",
      "  Surrogate splits:\n",
      "      Family     < 0.797   to the left,  agree=0.857, adj=0.705, (0 split)\n",
      "      GDP        < 8.6     to the left,  agree=0.835, adj=0.659, (0 split)\n",
      "      Freedom    < 0.75    to the left,  agree=0.736, adj=0.455, (0 split)\n",
      "      Generosity < -0.0695 to the right, agree=0.637, adj=0.250, (0 split)\n",
      "      Corruption < 0.8555  to the left,  agree=0.604, adj=0.182, (0 split)\n",
      "\n",
      "Node number 3: 58 observations,    complexity param=0.077711\n",
      "  mean=6.487638, MSE=0.4496749 \n",
      "  left son=6 (29 obs) right son=7 (29 obs)\n",
      "  Primary splits:\n",
      "      Dystopia.residual < 2.579   to the left,  improve=0.5085859, (0 missing)\n",
      "      Freedom           < 0.9015  to the left,  improve=0.4039614, (0 missing)\n",
      "      GDP               < 10.633  to the left,  improve=0.3997945, (0 missing)\n",
      "      Corruption        < 0.504   to the right, improve=0.3833382, (0 missing)\n",
      "      Family            < 0.933   to the left,  improve=0.3236853, (0 missing)\n",
      "  Surrogate splits:\n",
      "      GDP        < 10.617  to the left,  agree=0.707, adj=0.414, (0 split)\n",
      "      Corruption < 0.7245  to the right, agree=0.690, adj=0.379, (0 split)\n",
      "      Freedom    < 0.8955  to the left,  agree=0.672, adj=0.345, (0 split)\n",
      "      Family     < 0.933   to the left,  agree=0.655, adj=0.310, (0 split)\n",
      "      Generosity < -0.002  to the left,  agree=0.655, adj=0.310, (0 split)\n",
      "\n",
      "Node number 4: 44 observations,    complexity param=0.05496191\n",
      "  mean=4.383955, MSE=0.4211752 \n",
      "  left son=8 (12 obs) right son=9 (32 obs)\n",
      "  Primary splits:\n",
      "      Dystopia.residual < 2.064   to the left,  improve=0.5062380, (0 missing)\n",
      "      Family            < 0.633   to the left,  improve=0.2224289, (0 missing)\n",
      "      Freedom           < 0.686   to the left,  improve=0.1900058, (0 missing)\n",
      "      GDP               < 8.059   to the left,  improve=0.1865890, (0 missing)\n",
      "      Generosity        < -0.085  to the left,  improve=0.1237308, (0 missing)\n",
      "  Surrogate splits:\n",
      "      Freedom    < 0.8155  to the right, agree=0.795, adj=0.250, (0 split)\n",
      "      Health     < 60.608  to the right, agree=0.773, adj=0.167, (0 split)\n",
      "      Corruption < 0.592   to the left,  agree=0.773, adj=0.167, (0 split)\n",
      "      Generosity < -0.1185 to the left,  agree=0.750, adj=0.083, (0 split)\n",
      "\n",
      "Node number 5: 47 observations,    complexity param=0.04847301\n",
      "  mean=5.430128, MSE=0.311258 \n",
      "  left son=10 (22 obs) right son=11 (25 obs)\n",
      "  Primary splits:\n",
      "      Dystopia.residual < 2.278   to the left,  improve=0.5655749, (0 missing)\n",
      "      Freedom           < 0.776   to the left,  improve=0.2589117, (0 missing)\n",
      "      Family            < 0.8075  to the left,  improve=0.2196913, (0 missing)\n",
      "      Generosity        < -0.1585 to the left,  improve=0.2052383, (0 missing)\n",
      "      Health            < 67.378  to the left,  improve=0.1033932, (0 missing)\n",
      "  Surrogate splits:\n",
      "      Freedom    < 0.763   to the left,  agree=0.702, adj=0.364, (0 split)\n",
      "      GDP        < 9.464   to the right, agree=0.681, adj=0.318, (0 split)\n",
      "      Generosity < -0.1585 to the left,  agree=0.681, adj=0.318, (0 split)\n",
      "      Family     < 0.8115  to the left,  agree=0.660, adj=0.273, (0 split)\n",
      "      Health     < 68.8995 to the right, agree=0.596, adj=0.136, (0 split)\n",
      "\n",
      "Node number 6: 29 observations,    complexity param=0.01444021\n",
      "  mean=6.009414, MSE=0.2137254 \n",
      "  left son=12 (12 obs) right son=13 (17 obs)\n",
      "  Primary splits:\n",
      "      Dystopia.residual < 2.265   to the left,  improve=0.3976747, (0 missing)\n",
      "      GDP               < 10.297  to the left,  improve=0.3376992, (0 missing)\n",
      "      Freedom           < 0.809   to the left,  improve=0.3081262, (0 missing)\n",
      "      Family            < 0.9315  to the left,  improve=0.2723684, (0 missing)\n",
      "      Corruption        < 0.7605  to the right, improve=0.2120768, (0 missing)\n",
      "  Surrogate splits:\n",
      "      Family     < 0.89    to the left,  agree=0.724, adj=0.333, (0 split)\n",
      "      Health     < 74.9    to the right, agree=0.690, adj=0.250, (0 split)\n",
      "      Freedom    < 0.736   to the left,  agree=0.690, adj=0.250, (0 split)\n",
      "      Corruption < 0.549   to the left,  agree=0.655, adj=0.167, (0 split)\n",
      "      GDP        < 10.2595 to the left,  agree=0.621, adj=0.083, (0 split)\n",
      "\n",
      "Node number 7: 29 observations,    complexity param=0.02546646\n",
      "  mean=6.965862, MSE=0.2282277 \n",
      "  left son=14 (10 obs) right son=15 (19 obs)\n",
      "  Primary splits:\n",
      "      Health     < 70.4035 to the left,  improve=0.6567661, (0 missing)\n",
      "      Freedom    < 0.9015  to the left,  improve=0.5544445, (0 missing)\n",
      "      Corruption < 0.5355  to the right, improve=0.5414381, (0 missing)\n",
      "      GDP        < 10.5275 to the left,  improve=0.4657685, (0 missing)\n",
      "      Generosity < -0.039  to the left,  improve=0.4533731, (0 missing)\n",
      "  Surrogate splits:\n",
      "      GDP        < 10.5275 to the left,  agree=0.828, adj=0.5, (0 split)\n",
      "      Corruption < 0.6785  to the right, agree=0.828, adj=0.5, (0 split)\n",
      "      Family     < 0.9005  to the left,  agree=0.793, adj=0.4, (0 split)\n",
      "      Freedom    < 0.8515  to the left,  agree=0.759, adj=0.3, (0 split)\n",
      "      Generosity < -0.039  to the left,  agree=0.759, adj=0.3, (0 split)\n",
      "\n",
      "Node number 8: 12 observations\n",
      "  mean=3.629917, MSE=0.2367037 \n",
      "\n",
      "Node number 9: 32 observations,    complexity param=0.01424894\n",
      "  mean=4.666719, MSE=0.1971815 \n",
      "  left son=18 (7 obs) right son=19 (25 obs)\n",
      "  Primary splits:\n",
      "      Family            < 0.633   to the left,  improve=0.38545590, (0 missing)\n",
      "      GDP               < 7.5555  to the left,  improve=0.31989840, (0 missing)\n",
      "      Freedom           < 0.628   to the left,  improve=0.30030320, (0 missing)\n",
      "      Dystopia.residual < 2.933   to the left,  improve=0.29314410, (0 missing)\n",
      "      Generosity        < 0.0895  to the left,  improve=0.08014269, (0 missing)\n",
      "  Surrogate splits:\n",
      "      GDP        < 7.028   to the left,  agree=0.844, adj=0.286, (0 split)\n",
      "      Freedom    < 0.628   to the left,  agree=0.844, adj=0.286, (0 split)\n",
      "      Corruption < 0.6725  to the left,  agree=0.812, adj=0.143, (0 split)\n",
      "\n",
      "Node number 10: 22 observations\n",
      "  mean=4.982864, MSE=0.1229663 \n",
      "\n",
      "Node number 11: 25 observations\n",
      "  mean=5.82372, MSE=0.146 \n",
      "\n",
      "Node number 12: 12 observations\n",
      "  mean=5.662417, MSE=0.1504831 \n",
      "\n",
      "Node number 13: 17 observations\n",
      "  mean=6.254353, MSE=0.1133787 \n",
      "\n",
      "Node number 14: 10 observations\n",
      "  mean=6.4322, MSE=0.05501536 \n",
      "\n",
      "Node number 15: 19 observations\n",
      "  mean=7.246737, MSE=0.09060925 \n",
      "\n",
      "Node number 18: 7 observations\n",
      "  mean=4.145714, MSE=0.1996882 \n",
      "\n",
      "Node number 19: 25 observations\n",
      "  mean=4.8126, MSE=0.09919352 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(rpart)\n",
    "regressor_dt = rpart(formula = Score ~ .,\n",
    "                  data = dataset,\n",
    "                  control = rpart.control(minsplit = 10))\n",
    "summary(regressor_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'rpart.plot' is in use and will not be installed\"\n"
     ]
    }
   ],
   "source": [
    "install.packages(\"rpart.plot\")\n",
    "library(rpart.plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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O967m1hRjujWsOu1S0xdL3rum5uWUY7n2KzLtYuEXS+6Tpvb0EmO5tyoy7XMIvr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\nRK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(rpart.plot)\n",
    "prp(regressor_dt)\n",
    "ggsave(\"dtree.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Saving 6.67 x 6.67 in image\n",
      "\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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3D1BQbQDKA5hOUMwNUXGEA7YcBcLwgd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\ndq0hTJLsWkOYJNm1/wPARW6Jg9jVDwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_dt = predict(regressor_dt, newdata = test_set)\n",
    "\n",
    "Pred_Actual_dt <- as.data.frame(cbind(Prediction = y_pred_dt, Actual = test_set$Score))\n",
    "\n",
    "\n",
    "gg.dt <- ggplot(Pred_Actual_dt, aes(Actual, Prediction )) +\n",
    "  geom_point() + theme_bw() + geom_abline() +\n",
    "  labs(title = \"Decision Tree Regression\", x = \"Actual happiness score\",\n",
    "       y = \"Predicted happiness score\") +\n",
    "  theme(plot.title = element_text(family = \"Helvetica\", face = \"bold\", size = (15)), \n",
    "        axis.title = element_text(family = \"Helvetica\", size = (10)))\n",
    "gg.dt\n",
    "ggsave(\"dtmodel.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Country</th><th scope=col>Continent</th><th scope=col>Score</th><th scope=col>GDP</th><th scope=col>Family</th><th scope=col>Health</th><th scope=col>Freedom</th><th scope=col>Generosity</th><th scope=col>Corruption</th><th scope=col>Dystopia.residual</th><th scope=col>Rank</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Finland    </td><td>Europe</td><td>7.842</td><td>10.775</td><td>0.954</td><td>72.0</td><td>0.949</td><td>-0.098</td><td>0.186</td><td>3.253</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Denmark    </td><td>Europe</td><td>7.620</td><td>10.933</td><td>0.954</td><td>72.7</td><td>0.946</td><td> 0.030</td><td>0.179</td><td>2.868</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Switzerland</td><td>Europe</td><td>7.571</td><td>11.117</td><td>0.942</td><td>74.4</td><td>0.919</td><td> 0.025</td><td>0.292</td><td>2.839</td><td>3</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Iceland    </td><td>Europe</td><td>7.554</td><td>10.878</td><td>0.983</td><td>73.0</td><td>0.955</td><td> 0.160</td><td>0.673</td><td>2.967</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Netherlands</td><td>Europe</td><td>7.464</td><td>10.932</td><td>0.942</td><td>72.4</td><td>0.913</td><td> 0.175</td><td>0.338</td><td>2.798</td><td>5</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Norway     </td><td>Europe</td><td>7.392</td><td>11.053</td><td>0.954</td><td>73.3</td><td>0.960</td><td> 0.093</td><td>0.270</td><td>2.580</td><td>6</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & Country & Continent & Score & GDP & Family & Health & Freedom & Generosity & Corruption & Dystopia.residual & Rank\\\\\n",
       "  & <chr> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & Finland     & Europe & 7.842 & 10.775 & 0.954 & 72.0 & 0.949 & -0.098 & 0.186 & 3.253 & 1\\\\\n",
       "\t2 & Denmark     & Europe & 7.620 & 10.933 & 0.954 & 72.7 & 0.946 &  0.030 & 0.179 & 2.868 & 2\\\\\n",
       "\t3 & Switzerland & Europe & 7.571 & 11.117 & 0.942 & 74.4 & 0.919 &  0.025 & 0.292 & 2.839 & 3\\\\\n",
       "\t4 & Iceland     & Europe & 7.554 & 10.878 & 0.983 & 73.0 & 0.955 &  0.160 & 0.673 & 2.967 & 4\\\\\n",
       "\t5 & Netherlands & Europe & 7.464 & 10.932 & 0.942 & 72.4 & 0.913 &  0.175 & 0.338 & 2.798 & 5\\\\\n",
       "\t6 & Norway      & Europe & 7.392 & 11.053 & 0.954 & 73.3 & 0.960 &  0.093 & 0.270 & 2.580 & 6\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 11\n",
       "\n",
       "| <!--/--> | Country &lt;chr&gt; | Continent &lt;fct&gt; | Score &lt;dbl&gt; | GDP &lt;dbl&gt; | Family &lt;dbl&gt; | Health &lt;dbl&gt; | Freedom &lt;dbl&gt; | Generosity &lt;dbl&gt; | Corruption &lt;dbl&gt; | Dystopia.residual &lt;dbl&gt; | Rank &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | Finland     | Europe | 7.842 | 10.775 | 0.954 | 72.0 | 0.949 | -0.098 | 0.186 | 3.253 | 1 |\n",
       "| 2 | Denmark     | Europe | 7.620 | 10.933 | 0.954 | 72.7 | 0.946 |  0.030 | 0.179 | 2.868 | 2 |\n",
       "| 3 | Switzerland | Europe | 7.571 | 11.117 | 0.942 | 74.4 | 0.919 |  0.025 | 0.292 | 2.839 | 3 |\n",
       "| 4 | Iceland     | Europe | 7.554 | 10.878 | 0.983 | 73.0 | 0.955 |  0.160 | 0.673 | 2.967 | 4 |\n",
       "| 5 | Netherlands | Europe | 7.464 | 10.932 | 0.942 | 72.4 | 0.913 |  0.175 | 0.338 | 2.798 | 5 |\n",
       "| 6 | Norway      | Europe | 7.392 | 11.053 | 0.954 | 73.3 | 0.960 |  0.093 | 0.270 | 2.580 | 6 |\n",
       "\n"
      ],
      "text/plain": [
       "  Country     Continent Score GDP    Family Health Freedom Generosity\n",
       "1 Finland     Europe    7.842 10.775 0.954  72.0   0.949   -0.098    \n",
       "2 Denmark     Europe    7.620 10.933 0.954  72.7   0.946    0.030    \n",
       "3 Switzerland Europe    7.571 11.117 0.942  74.4   0.919    0.025    \n",
       "4 Iceland     Europe    7.554 10.878 0.983  73.0   0.955    0.160    \n",
       "5 Netherlands Europe    7.464 10.932 0.942  72.4   0.913    0.175    \n",
       "6 Norway      Europe    7.392 11.053 0.954  73.3   0.960    0.093    \n",
       "  Corruption Dystopia.residual Rank\n",
       "1 0.186      3.253             1   \n",
       "2 0.179      2.868             2   \n",
       "3 0.292      2.839             3   \n",
       "4 0.673      2.967             4   \n",
       "5 0.338      2.798             5   \n",
       "6 0.270      2.580             6   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(happ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Saving 6.67 x 6.67 in image\n",
      "\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "\"font family not found in Windows font database\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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qvX86DwqCPCaPAQ1bPew1R+O929Dgx3Xb2iB4VHHRFGg0eonvYepvLb6e51YLjf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\n1uEbJDkAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggpubr)\n",
    "ggarrange(gg.lm, gg.svr, gg.dt, ncol = 2, nrow = 2)\n",
    "ggsave(\"compare.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Installing package into 'C:/Users/tulik/Documents/R/win-library/4.0'\n",
      "(as 'lib' is unspecified)\n",
      "\n",
      "Warning message:\n",
      "\"package 'ggarrange' is not available (for R version 4.0.0)\"\n"
     ]
    },
    {
     "ename": "ERROR",
     "evalue": "Error in library(\"ggarrange\"): there is no package called 'ggarrange'\n",
     "output_type": "error",
     "traceback": [
      "Error in library(\"ggarrange\"): there is no package called 'ggarrange'\nTraceback:\n",
      "1. library(\"ggarrange\")"
     ]
    }
   ],
   "source": [
    "install.packages(\"ggarrange\")\n",
    "library(\"ggarrange\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.0.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}